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Ubuntu环境下C++使用onnxruntime和Opencv进行YOLOv8模型部署_ubuntu 安装onnx c++

作者：知新_RL | 2024-05-17 13:32:02

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ubuntu 安装onnx c++

目录

环境配置

系统环境

项目文件路径 

文件环境

 config.txt

 CMakeLists.txt

type.names

 读取config.txt配置文件

修改图片尺寸格式

读取缺陷标志文件

生成缺陷随机颜色标识

模型推理

推理结果获取

缺陷信息还原并显示

总代码

环境配置

系统环境

Ubuntu18.04

onnxruntime-linux-x64 1.12.1：https://github.com/microsoft/onnxruntime/releases

opencv 3.4.3

cmake 3.10.2

项目文件路径 

1.  bin：存放可执行程序和识别结果
2.  data：存放数据集
3.  src：存放源程序
4.  include：存放头文件
5.  config.txt：配置文件，内容分别是模型相对路径、图片相对路径、缺陷标识文件相对路径、缺陷识别阈值、缺陷重叠阈值
6.  type.names：缺陷标识文件，内容和模型识别的缺陷标识顺序需要一致

文件环境

 config.txt

分别表示模型相对路径、图片相对路径、缺陷标识文件相对路径、缺陷识别阈值、缺陷重叠阈值

../models/best.onnx
../data/2.bmp
../type.names
0.4
0.4

 CMakeLists.txt

需要更改的地方已经在里面标注好了

  1. # 项目名称，随便写
  2. PROJECT(image_onnx)
  3. # cmake版本，根据自己的写
  4. cmake_minimum_required(VERSION 3.10)
  5.  
  6. # 编译好的可执行文件放置的位置
  7. set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${image_onnx_SOURCE_DIR}/bin)
  8.  
  9. # find required opencv
  10. find_package(OpenCV REQUIRED)
  11. # directory of opencv headers
  12. include_directories(${OpenCV_INCLUDE_DIRS})
  13.  
  14. # 根据自己的onnxruntime存放路径编写
  15. set(ONNXRUNTIME_ROOT_PATH /home/ebaina/onnxruntime-linux-x64-1.12.1/)
  16. set(ONNXRUNTIME_INCLUDE_DIRS ${ONNXRUNTIME_ROOT_PATH}/include/)
  17. set(ONNXRUNTIME_LIB ${ONNXRUNTIME_ROOT_PATH}lib/libonnxruntime.so)
  18.  
  19. # 需要编译的cpp文件所在路径，前面是编译好的可执行文件名
  20. add_executable(image_onnx src/main_image.cpp
  21. src/change_image.cpp
  22. src/adjust_result.cpp)
  23. # directory of opencv library
  24. link_directories(${OpenCV_LIBRARY_DIRS})
  25. # opencv libraries
  26. target_link_libraries(image_onnx ${OpenCV_LIBS})
  27.  
  28. include_directories(${ONNXRUNTIME_INCLUDE_DIRS})
  29. target_link_libraries(image_onnx ${ONNXRUNTIME_LIB})
  30.  
  31. # include
  32. target_include_directories(image_onnx
  33.     PRIVATE 
  34.         ${PROJECT_SOURCE_DIR}/include
  35. )

type.names

缺陷标志文件，内容和模型识别的缺陷标识顺序需要一致，模型识别网站：Netron

burr
cbreakage
inbreakage
bpulp
corrode

 读取config.txt配置文件

  1.     // 自动读取模型路径，图片路径，缺陷阈值，重叠阈值
  2.     std::string model_path_;
  3.     std::string imgPath;
  4.     std::string namesPath;
  5.     float threshold;
  6.     float nms_threshold;
  7.         // 打开配置文件并读取配置
  8.     std::ifstream configFile("../config.txt");
  9.     if (configFile.is_open()) {
  10.         configFile >> model_path_ >> imgPath >> namesPath >> threshold >> nms_threshold;
  11.         configFile.close();
  12.  
  13.         std::cout << "Model Path: " << model_path_ << std::endl;
  14.         std::cout << "Image Path: " << imgPath << std::endl;
  15.         std::cout << "Names Path: " << namesPath << std::endl;
  16.         std::cout << "Threshold: " << threshold << std::endl;
  17.         std::cout << "NMS Threshold: " << nms_threshold << std::endl;
  18.     } else
  19.         std::cerr << "Failed to open config file." << std::endl;
  20.     const char* model_path = model_path_.c_str();

修改图片尺寸格式

  1.     // 图片变换
  2.     cv::Mat inputImage = cv::imread(imgPath);
  3.     if (inputImage.empty()) {
  4.         std::cerr << "Failed to load image." << std::endl;
  5.         return 1;
  6.     }
  7.         // 获取图片尺寸
  8.     int y = inputImage.rows;
  9.     int x = inputImage.cols;
  10.         // 图片尺寸变换
  11.     cv::Mat image0 = resizeImage(inputImage, y, x);
  12.         // 图像归一化
  13.     std::vector<float> input_image_ = nchwImage(image0);

读取缺陷标志文件

  1.     // 读取缺陷标志文件
  2.     std::ifstream inputFile(namesPath);
  3.     if (!inputFile.is_open()) {
  4.         std::cerr << "Failed to open the file." << std::endl;
  5.         return 1;
  6.     }
  7.     std::vector<std::string> typeNames;
  8.     std::string line;
  9.     while (std::getline(inputFile, line)) 
  10.         typeNames.push_back(line);
  11.     inputFile.close();

生成缺陷随机颜色标识

  1.     // 缺陷颜色标识随机
  2.     int numColors = typeNames.size();
  3.     std::vector<std::vector<int>> colors;
  4.     for (int i = 0; i < numColors; ++i) 
  5.         colors.push_back(generateRandomColor());
  6.     //     // 打印颜色种类
  7.     // for (const auto &color : colors) 
  8.     //     std::cout << "R: " << color[0] << ", G: " << color[1] << ", B: " << color[2] << std::endl;

模型推理

  1.     // 模型设置和推理结果
  2.     Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "Default");
  3.         // CPU
  4.     Ort::Session session_{env, model_path, Ort::SessionOptions{nullptr}}; 
  5.         // 模型输入尺寸
  6.     static constexpr const int height_ = 640; //model input height
  7.     static constexpr const int width_ = 640; //model input width
  8.     Ort::Value input_tensor_{nullptr};
  9.     std::array<int64_t, 4> input_shape_{1, 3, height_, width_}; //mode input shape NCHW = 1x3xHxW
  10.         // 模型输出尺寸
  11.     Ort::Value output_tensor_{nullptr};
  12.     std::array<int64_t, 3> output_shape_{1, 9, 8400}; //model output shape,
  13.     std::array<_Float32, 9*8400> results_{};
  14.  
  15.         // 模型输入输出张量设置
  16.     auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
  17.     input_tensor_ = Ort::Value::CreateTensor<float>(memory_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size());
  18.     output_tensor_ = Ort::Value::CreateTensor<float>(memory_info, results_.data(), results_.size(), output_shape_.data(), output_shape_.size());
  19.         // 查看模型输入输出的名称
  20.     const char* input_names[] = {"images"};
  21.     const char* output_names[] = {"output0"};
  22.         // 推理
  23.     session_.Run(Ort::RunOptions{nullptr}, input_names, &input_tensor_, 1, output_names, &output_tensor_, 1);
  24.     float* out = output_tensor_.GetTensorMutableData<float>();

推理结果获取

  1.         // 推理结果获取
  2.     int rows = 9;      // 第二维度大小，即行数
  3.     int cols = 8400;   // 第三维度大小，即列数
  4.     std::vector<std::vector<float>> matrix(rows, std::vector<float>(cols));
  5.     for (int row = 0; row < rows; ++row) 
  6.         for (int col = 0; col < cols; ++col) 
  7.             matrix[row][col] = out[row * cols + col];
  8.         // 9，8400数组转置为8400，9
  9.     std::vector<std::vector<float>> tran_matrix = transpose(matrix);
  10.     //     // 显示缺陷筛选结果
  11.     // std::vector<std::vector<float>> num = tran_matrix;
  12.     // for (size_t n = 0; n < num.size(); ++n) {
  13.     //     bool aboveThreshold = false;
  14.     //     for (size_t col = 4; col <= 8; ++col)
  15.     //         if (num[n][col] > threshold) {
  16.     //             aboveThreshold = true;
  17.     //             break;
  18.     //         }
  19.         
  20.     //     if (aboveThreshold) {
  21.     //         std::cout << "Row " << n << ": ";
  22.     //         for (const auto& val : num[n]) 
  23.     //             std::cout << val << " ";
  24.                 
  25.     //         std::cout << std::endl;
  26.     //     }
  27.     // }

缺陷信息还原并显示

  1.     // 缺陷还原
  2.     std::vector<std::vector<double>> select_matrix;
  3.     select_matrix = select(tran_matrix, threshold, cols,rows);
  4.         // 缺陷位置信息还原
  5.     select_matrix = return_(select_matrix, y, x);
  6.         // 缺陷位置信息筛选
  7.     select_matrix = nms_(select_matrix, nms_threshold);
  8.     //     // 打印数组的内容
  9.     // for (const auto& row : select_matrix){
  10.     //     for (const auto& value : row) {
  11.     //         std::cout << value << " ";
  12.     //     }
  13.     //     std::cout << std::endl;
  14.     // }
  15.         // 绘制识别框
  16.     cv::Mat outputImage = draw_image(select_matrix, inputImage, typeNames, colors);
  17.     
  18.     // 自定义窗口大小
  19.     int windowWidth = 1200;
  20.     int windowHeight = 900;
  21.  
  22.     // 调整窗口大小
  23.     cv::namedWindow("Image with Bounding Boxes", cv::WINDOW_NORMAL);
  24.     cv::resizeWindow("Image with Bounding Boxes", windowWidth, windowHeight);
  25.     cv::imshow("Image with Bounding Boxes", outputImage);
  26.     cv::imwrite("marked_image.jpg", outputImage);
  27.     cv::waitKey(0);

main代码(关注取源码!)

  1. #include <assert.h>
  2. #include <random>
  3. #include <onnxruntime_cxx_api.h>
  4. #include "cpu_provider_factory.h"
  5. #include <adjust_result.h>
  6.  
  7. // 随机生成颜色
  8. std::vector<int> generateRandomColor() {
  9.     std::random_device rd;
  10.     std::mt19937 gen(rd());
  11.     std::uniform_real_distribution<double> dis(0.0, 1.0);
  12.  
  13.     std::vector<int> color(3);
  14.     for (int i = 0; i < 3; ++i) {
  15.         color[i] = static_cast<int>(dis(gen) * 255);
  16.     }
  17.  
  18.     return color;
  19. }
  20.  
  21. int main(int argc, char* argv[]) {
  22.     // // 模型路径，图片路径，缺陷阈值，重叠阈值
  23.     // const char* model_path = "../models/best.onnx";
  24.     // std::string imgPath = "../data/3.bmp";
  25.     // std::string namesPath = "../type.names";
  26.     // float threshold = 0.4;
  27.     // float nms_threshold = 0.4;
  28.     // 自动读取模型路径，图片路径，缺陷阈值，重叠阈值
  29.     std::string model_path_;
  30.     std::string imgPath;
  31.     std::string namesPath;
  32.     float threshold;
  33.     float nms_threshold;
  34.         // 打开配置文件并读取配置
  35.     std::ifstream configFile("../config.txt");
  36.     if (configFile.is_open()) {
  37.         configFile >> model_path_ >> imgPath >> namesPath >> threshold >> nms_threshold;
  38.         configFile.close();
  39.  
  40.         std::cout << "Model Path: " << model_path_ << std::endl;
  41.         std::cout << "Image Path: " << imgPath << std::endl;
  42.         std::cout << "Names Path: " << namesPath << std::endl;
  43.         std::cout << "Threshold: " << threshold << std::endl;
  44.         std::cout << "NMS Threshold: " << nms_threshold << std::endl;
  45.     } else
  46.         std::cerr << "Failed to open config file." << std::endl;
  47.     const char* model_path = model_path_.c_str();
  48.  
  49.     // 图片变换
  50.     cv::Mat inputImage = cv::imread(imgPath);
  51.     if (inputImage.empty()) {
  52.         std::cerr << "Failed to load image." << std::endl;
  53.         return 1;
  54.     }
  55.         // 获取图片尺寸
  56.     int y = inputImage.rows;
  57.     int x = inputImage.cols;
  58.         // 图片尺寸变换
  59.     cv::Mat image0 = resizeImage(inputImage, y, x);
  60.         // 图像归一化
  61.     std::vector<float> input_image_ = nchwImage(image0);
  62.     
  63.     // 读取缺陷标志文件
  64.     std::ifstream inputFile(namesPath);
  65.     if (!inputFile.is_open()) {
  66.         std::cerr << "Failed to open the file." << std::endl;
  67.         return 1;
  68.     }
  69.     std::vector<std::string> typeNames;
  70.     std::string line;
  71.     while (std::getline(inputFile, line)) 
  72.         typeNames.push_back(line);
  73.     inputFile.close();
  74.     //     // 打印缺陷标志文件内容
  75.     // std::cout << "Number of elements: " << typeNames.size() << std::endl;
  76.     // for (const std::string &typeName : typeNames) 
  77.     //     std::cout << typeName << std::endl;
  78.  
  79.     // 缺陷颜色标识随机
  80.     int numColors = typeNames.size();
  81.     std::vector<std::vector<int>> colors;
  82.     for (int i = 0; i < numColors; ++i) 
  83.         colors.push_back(generateRandomColor());
  84.     //     // 打印颜色种类
  85.     // for (const auto &color : colors) 
  86.     //     std::cout << "R: " << color[0] << ", G: " << color[1] << ", B: " << color[2] << std::endl;
  87.  
  88.     // 模型设置和推理结果
  89.     Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "Default");
  90.         // CPU
  91.     Ort::Session session_{env, model_path, Ort::SessionOptions{nullptr}}; 
  92.         // 模型输入尺寸
  93.     static constexpr const int height_ = 640; //model input height
  94.     static constexpr const int width_ = 640; //model input width
  95.     Ort::Value input_tensor_{nullptr};
  96.     std::array<int64_t, 4> input_shape_{1, 3, height_, width_}; //mode input shape NCHW = 1x3xHxW
  97.         // 模型输出尺寸
  98.     Ort::Value output_tensor_{nullptr};
  99.     std::array<int64_t, 3> output_shape_{1, 9, 8400}; //model output shape,
  100.     std::array<_Float32, 9*8400> results_{};
  101.  
  102.         // 模型输入输出张量设置
  103.     auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
  104.     input_tensor_ = Ort::Value::CreateTensor<float>(memory_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size());
  105.     output_tensor_ = Ort::Value::CreateTensor<float>(memory_info, results_.data(), results_.size(), output_shape_.data(), output_shape_.size());
  106.         // 查看模型输入输出的名称
  107.     const char* input_names[] = {"images"};
  108.     const char* output_names[] = {"output0"};
  109.         // 推理
  110.     session_.Run(Ort::RunOptions{nullptr}, input_names, &input_tensor_, 1, output_names, &output_tensor_, 1);
  111.     float* out = output_tensor_.GetTensorMutableData<float>();
  112.  
  113.         // 推理结果获取
  114.     int rows = 9;      // 第二维度大小，即行数
  115.     int cols = 8400;   // 第三维度大小，即列数
  116.     std::vector<std::vector<float>> matrix(rows, std::vector<float>(cols));
  117.     for (int row = 0; row < rows; ++row) 
  118.         for (int col = 0; col < cols; ++col) 
  119.             matrix[row][col] = out[row * cols + col];
  120.         // 9，8400数组转置为8400，9
  121.     std::vector<std::vector<float>> tran_matrix = transpose(matrix);
  122.     //     // 显示缺陷筛选结果
  123.     // std::vector<std::vector<float>> num = tran_matrix;
  124.     // for (size_t n = 0; n < num.size(); ++n) {
  125.     //     bool aboveThreshold = false;
  126.     //     for (size_t col = 4; col <= 8; ++col)
  127.     //         if (num[n][col] > threshold) {
  128.     //             aboveThreshold = true;
  129.     //             break;
  130.     //         }
  131.         
  132.     //     if (aboveThreshold) {
  133.     //         std::cout << "Row " << n << ": ";
  134.     //         for (const auto& val : num[n]) 
  135.     //             std::cout << val << " ";
  136.                 
  137.     //         std::cout << std::endl;
  138.     //     }
  139.     // }
  140.  
  141.     // 缺陷还原
  142.     std::vector<std::vector<double>> select_matrix;
  143.     select_matrix = select(tran_matrix, threshold, cols,rows);
  144.         // 缺陷位置信息还原
  145.     select_matrix = return_(select_matrix, y, x);
  146.         // 缺陷位置信息筛选
  147.     select_matrix = nms_(select_matrix, nms_threshold);
  148.     //     // 打印数组的内容
  149.     // for (const auto& row : select_matrix){
  150.     //     for (const auto& value : row) {
  151.     //         std::cout << value << " ";
  152.     //     }
  153.     //     std::cout << std::endl;
  154.     // }
  155.         // 绘制识别框
  156.     cv::Mat outputImage = draw_image(select_matrix, inputImage, typeNames, colors);
  157.     
  158.     // 自定义窗口大小
  159.     int windowWidth = 1200;
  160.     int windowHeight = 900;
  161.  
  162.     // 调整窗口大小
  163.     cv::namedWindow("Image with Bounding Boxes", cv::WINDOW_NORMAL);
  164.     cv::resizeWindow("Image with Bounding Boxes", windowWidth, windowHeight);
  165.     cv::imshow("Image with Bounding Boxes", outputImage);
  166.     cv::imwrite("marked_image.jpg", outputImage);
  167.     cv::waitKey(0);
  168.  
  169.     return 0;
  170. }
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