├── CMakeLists.txt ├── README.md ├── best_yolox_nano.onnx ├── demo.jpg └── main.cpp /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | 2 | cmake_minimum_required(VERSION 3.13) 3 | project(onnx_c++) 4 | 5 | 6 | find_package(OpenCV REQUIRED) 7 | find_path(ONNX_RUNTIME_SESSION_INCLUDE_DIRS onnxruntime_cxx_api.h HINTS /usr/local/include/onnxruntime/core/session/) 8 | 9 | find_library(ONNX_RUNTIME_LIB onnxruntime HINTS /usr/local/lib) 10 | 11 | add_executable(onnx_c++ main.cpp) 12 | target_include_directories(onnx_c++ PRIVATE ${ONNX_RUNTIME_SESSION_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS}) 13 | target_link_libraries(onnx_c++ PRIVATE ${ONNX_RUNTIME_LIB} ${OpenCV_LIBRARIES}) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # onnx_c++ 2 | 3 | This repo is contains code used alongside with our object detection course. It permits practitioners use an onnx model(yolox) in c++ 4 | -------------------------------------------------------------------------------- /best_yolox_nano.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Neuralearn/onnx_cplusplus/e07d0b0fda53fabd5d0126a2e5c1d955cfbb4d11/best_yolox_nano.onnx -------------------------------------------------------------------------------- /demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Neuralearn/onnx_cplusplus/e07d0b0fda53fabd5d0126a2e5c1d955cfbb4d11/demo.jpg -------------------------------------------------------------------------------- /main.cpp: -------------------------------------------------------------------------------- 1 | // https://github.com/microsoft/onnxruntime/blob/v1.8.2/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp 2 | // https://github.com/microsoft/onnxruntime/blob/v1.8.2/include/onnxruntime/core/session/onnxruntime_cxx_api.h 3 | #include 4 | 5 | #include 6 | #include 7 | #include 8 | 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | #include 15 | #include 16 | #include 17 | 18 | #include 19 | 20 | #include 21 | #include 22 | #include 23 | #include 24 | 25 | 26 | 27 | 28 | #include 29 | #include 30 | #include 31 | #include 32 | #include 33 | 34 | /** 35 | * @brief Define names based depends on Unicode path support 36 | */ 37 | #define tcout std::cout 38 | #define file_name_t std::string 39 | #define imread_t cv::imread 40 | #define NMS_THRESH 0.5 41 | #define BBOX_CONF_THRESH 0.3 42 | 43 | static const int INPUT_W = 416; 44 | static const int INPUT_H = 416; 45 | static const int NUM_CLASSES = 80; // COCO has 80 classes. Modify this value on your own dataset. 46 | 47 | template 48 | T vectorProduct(const std::vector& v) 49 | { 50 | return accumulate(v.begin(), v.end(), 1, std::multiplies()); 51 | } 52 | 53 | /** 54 | * @brief Operator overloading for printing vectors 55 | * @tparam T 56 | * @param os 57 | * @param v 58 | * @return std::ostream& 59 | */ 60 | template 61 | std::ostream& operator<<(std::ostream& os, const std::vector& v) 62 | { 63 | os << "["; 64 | for (int i = 0; i < v.size(); ++i) 65 | { 66 | os << v[i]; 67 | if (i != v.size() - 1) 68 | { 69 | os << ", "; 70 | } 71 | } 72 | os << "]"; 73 | return os; 74 | } 75 | 76 | 77 | cv::Mat static_resize(cv::Mat& img) { 78 | float r = std::min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0)); 79 | // r = std::min(r, 1.0f); 80 | int unpad_w = r * img.cols; 81 | int unpad_h = r * img.rows; 82 | cv::Mat re(unpad_h, unpad_w, CV_8UC3); 83 | cv::resize(img, re, re.size()); 84 | //cv::Mat out(INPUT_W, INPUT_H, CV_8UC3, cv::Scalar(114, 114, 114)); 85 | cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(114, 114, 114)); 86 | re.copyTo(out(cv::Rect(0, 0, re.cols, re.rows))); 87 | return out; 88 | } 89 | 90 | struct Object 91 | { 92 | cv::Rect_ rect; 93 | int label; 94 | float prob; 95 | }; 96 | 97 | struct GridAndStride 98 | { 99 | int grid0; 100 | int grid1; 101 | int stride; 102 | }; 103 | 104 | static void generate_grids_and_stride(const int target_w, const int target_h, std::vector& strides, std::vector& grid_strides) 105 | { 106 | for (auto stride : strides) 107 | { 108 | int num_grid_w = target_w / stride; 109 | int num_grid_h = target_h / stride; 110 | for (int g1 = 0; g1 < num_grid_h; g1++) 111 | { 112 | for (int g0 = 0; g0 < num_grid_w; g0++) 113 | { 114 | grid_strides.push_back((GridAndStride){g0, g1, stride}); 115 | } 116 | } 117 | } 118 | } 119 | 120 | 121 | static void generate_yolox_proposals(std::vector grid_strides, const float* feat_ptr, float prob_threshold, std::vector