Improve YOLOv8 ONNX Runtime c++ example for all OS with `CmakeList.txt` support (#4274)

Signed-off-by: Onuralp SEZER <thunderbirdtr@fedoraproject.org>
Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
single_channel
Onuralp SEZER 1 year ago committed by GitHub
parent c9be1f3cce
commit 22474e9ad5
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23

@ -0,0 +1,71 @@
cmake_minimum_required(VERSION 3.5)
set(PROJECT_NAME Yolov8OnnxRuntimeCPPInference)
project(${PROJECT_NAME} VERSION 0.0.1 LANGUAGES CXX)
# -------------- Support C++17 for using filesystem ------------------#
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS ON)
set(CMAKE_INCLUDE_CURRENT_DIR ON)
# OpenCV
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
# ONNXRUNTIME
# Set ONNXRUNTIME_VERSION
set(ONNXRUNTIME_VERSION 1.15.1)
if(WIN32)
# CPU
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-win-x64-${ONNXRUNTIME_VERSION}")
# GPU
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-win-x64-gpu-${ONNXRUNTIME_VERSION}")
elseif(LINUX)
# CPU
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}")
# GPU
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-linux-x64-gpu-${ONNXRUNTIME_VERSION}")
elseif(APPLE)
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-osx-arm64-${ONNXRUNTIME_VERSION}")
# Apple X64 binary
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-osx-x64-${ONNXRUNTIME_VERSION}")
# Apple Universal binary
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-osx-universal2-${ONNXRUNTIME_VERSION}")
endif()
include_directories(${PROJECT_NAME} ${ONNXRUNTIME_ROOT}/include)
set(PROJECT_SOURCES
main.cpp
inference.h
inference.cpp
)
add_executable(${PROJECT_NAME} ${PROJECT_SOURCES})
if(WIN32)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/onnxruntime.lib)
elseif(LINUX)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/libonnxruntime.so)
elseif(APPLE)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/libonnxruntime.dylib)
endif()
# For windows system, copy onnxruntime.dll to the same folder of the executable file
if(WIN32)
add_custom_command(TARGET ${PROJECT_NAME} POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${ONNXRUNTIME_ROOT}/lib/onnxruntime.dll"
$<TARGET_FILE_DIR:${PROJECT_NAME}>)
endif()
# Download https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml
# and put it in the same folder of the executable file
configure_file(coco.yaml ${CMAKE_CURRENT_BINARY_DIR}/coco.yaml COPYONLY)

@ -2,8 +2,6 @@
This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API. This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.
We recommend using Visual Studio to build the project.
## Benefits ## Benefits
- Friendly for deployment in the industrial sector. - Friendly for deployment in the industrial sector.
@ -25,13 +23,20 @@ model = YOLO("yolov8n.pt")
model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640) model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
``` ```
Alternatively, you can use the following command for exporting the model in the terminal
```bash
yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640
```
## Dependencies ## Dependencies
| Dependency | Version | | Dependency | Version |
| ----------------------- | -------- | | -------------------------------- | -------- |
| Onnxruntime-win-x64-gpu | >=1.14.1 | | Onnxruntime(linux,windows,macos) | >=1.14.1 |
| OpenCV | >=4.0.0 | | OpenCV | >=4.0.0 |
| C++ | >=17 | | C++ | >=17 |
| Cmake | >=3.5 |
Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature. Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.
@ -39,9 +44,9 @@ Note: The dependency on C++17 is due to the usage of the C++17 filesystem featur
```c++ ```c++
// CPU inference // CPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, false}; DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, false};
// GPU inference // GPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, true}; DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, true};
// Load your image // Load your image
cv::Mat img = cv::imread(img_path); cv::Mat img = cv::imread(img_path);

@ -2,7 +2,6 @@
#include <regex> #include <regex>
#define benchmark #define benchmark
#define ELOG
DCSP_CORE::DCSP_CORE() DCSP_CORE::DCSP_CORE()
{ {
@ -29,7 +28,7 @@ char* BlobFromImage(cv::Mat& iImg, T& iBlob)
{ {
for (int w = 0; w < imgWidth; w++) for (int w = 0; w < imgWidth; w++)
{ {
iBlob[c * imgWidth * imgHeight + h * imgWidth + w] = (std::remove_pointer<T>::type)((iImg.at<cv::Vec3b>(h, w)[c]) / 255.0f); iBlob[c * imgWidth * imgHeight + h * imgWidth + w] = typename std::remove_pointer<T>::type((iImg.at<cv::Vec3b>(h, w)[c]) / 255.0f);
} }
} }
} }
@ -75,17 +74,21 @@ char* DCSP_CORE::CreateSession(DCSP_INIT_PARAM &iParams)
OrtCUDAProviderOptions cudaOption; OrtCUDAProviderOptions cudaOption;
cudaOption.device_id = 0; cudaOption.device_id = 0;
sessionOption.AppendExecutionProvider_CUDA(cudaOption); sessionOption.AppendExecutionProvider_CUDA(cudaOption);
//OrtOpenVINOProviderOptions ovOption;
//sessionOption.AppendExecutionProvider_OpenVINO(ovOption);
} }
sessionOption.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL); sessionOption.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
sessionOption.SetIntraOpNumThreads(iParams.IntraOpNumThreads); sessionOption.SetIntraOpNumThreads(iParams.IntraOpNumThreads);
sessionOption.SetLogSeverityLevel(iParams.LogSeverityLevel); sessionOption.SetLogSeverityLevel(iParams.LogSeverityLevel);
#ifdef _WIN32
int ModelPathSize = MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), nullptr, 0); int ModelPathSize = MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), nullptr, 0);
wchar_t* wide_cstr = new wchar_t[ModelPathSize + 1]; wchar_t* wide_cstr = new wchar_t[ModelPathSize + 1];
MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), wide_cstr, ModelPathSize); MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), wide_cstr, ModelPathSize);
wide_cstr[ModelPathSize] = L'\0'; wide_cstr[ModelPathSize] = L'\0';
const wchar_t* modelPath = wide_cstr; const wchar_t* modelPath = wide_cstr;
#else
const char* modelPath = iParams.ModelPath.c_str();
#endif // _WIN32
session = new Ort::Session(env, modelPath, sessionOption); session = new Ort::Session(env, modelPath, sessionOption);
Ort::AllocatorWithDefaultOptions allocator; Ort::AllocatorWithDefaultOptions allocator;
size_t inputNodesNum = session->GetInputCount(); size_t inputNodesNum = session->GetInputCount();
@ -96,7 +99,6 @@ char* DCSP_CORE::CreateSession(DCSP_INIT_PARAM &iParams)
strcpy(temp_buf, input_node_name.get()); strcpy(temp_buf, input_node_name.get());
inputNodeNames.push_back(temp_buf); inputNodeNames.push_back(temp_buf);
} }
size_t OutputNodesNum = session->GetOutputCount(); size_t OutputNodesNum = session->GetOutputCount();
for (size_t i = 0; i < OutputNodesNum; i++) for (size_t i = 0; i < OutputNodesNum; i++)
{ {
@ -151,7 +153,7 @@ char* DCSP_CORE::RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT>& oResult)
template<typename N> template<typename N>
char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult) char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult)
{ {
Ort::Value inputTensor = Ort::Value::CreateTensor<std::remove_pointer<N>::type>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), inputNodeDims.data(), inputNodeDims.size()); Ort::Value inputTensor = Ort::Value::CreateTensor<typename std::remove_pointer<N>::type>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), inputNodeDims.data(), inputNodeDims.size());
#ifdef benchmark #ifdef benchmark
clock_t starttime_2 = clock(); clock_t starttime_2 = clock();
#endif // benchmark #endif // benchmark
@ -159,10 +161,11 @@ char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std
#ifdef benchmark #ifdef benchmark
clock_t starttime_3 = clock(); clock_t starttime_3 = clock();
#endif // benchmark #endif // benchmark
Ort::TypeInfo typeInfo = outputTensor.front().GetTypeInfo(); Ort::TypeInfo typeInfo = outputTensor.front().GetTypeInfo();
auto tensor_info = typeInfo.GetTensorTypeAndShapeInfo(); auto tensor_info = typeInfo.GetTensorTypeAndShapeInfo();
std::vector<int64_t>outputNodeDims = tensor_info.GetShape(); std::vector<int64_t>outputNodeDims = tensor_info.GetShape();
std::remove_pointer<N>::type* output = outputTensor.front().GetTensorMutableData<std::remove_pointer<N>::type>(); auto output = outputTensor.front().GetTensorMutableData<typename std::remove_pointer<N>::type>();
delete blob; delete blob;
switch (modelType) switch (modelType)
{ {
@ -183,7 +186,7 @@ char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std
for (int i = 0; i < strideNum; ++i) for (int i = 0; i < strideNum; ++i)
{ {
float* classesScores = data + 4; float* classesScores = data + 4;
cv::Mat scores(1, classesNum, CV_32FC1, classesScores); cv::Mat scores(1, this->classes.size(), CV_32FC1, classesScores);
cv::Point class_id; cv::Point class_id;
double maxClassScore; double maxClassScore;
cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id); cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
@ -203,13 +206,14 @@ char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std
int width = int(w * x_factor); int width = int(w * x_factor);
int height = int(h * y_factor); int height = int(h * y_factor);
boxes.push_back(cv::Rect(left, top, width, height)); boxes.emplace_back(left, top, width, height);
} }
data += signalResultNum; data += signalResultNum;
} }
std::vector<int> nmsResult; std::vector<int> nmsResult;
cv::dnn::NMSBoxes(boxes, confidences, rectConfidenceThreshold, iouThreshold, nmsResult); cv::dnn::NMSBoxes(boxes, confidences, rectConfidenceThreshold, iouThreshold, nmsResult);
for (int i = 0; i < nmsResult.size(); ++i) for (int i = 0; i < nmsResult.size(); ++i)
{ {
int idx = nmsResult[i]; int idx = nmsResult[i];
@ -266,6 +270,5 @@ char* DCSP_CORE::WarmUpSession()
std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl; std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
} }
} }
return Ret; return Ret;
} }

@ -1,15 +1,17 @@
#pragma once #pragma once
#define _CRT_SECURE_NO_WARNINGS
#define RET_OK nullptr #define RET_OK nullptr
#ifdef _WIN32
#include <Windows.h>
#include <direct.h>
#include <io.h>
#endif
#include <string> #include <string>
#include <vector> #include <vector>
#include <stdio.h> #include <cstdio>
#include "io.h" #include <opencv2/opencv.hpp>
#include "direct.h"
#include "opencv.hpp"
#include <Windows.h>
#include "onnxruntime_cxx_api.h" #include "onnxruntime_cxx_api.h"
@ -23,13 +25,12 @@ enum MODEL_TYPE
}; };
typedef struct _DCSP_INIT_PARAM typedef struct _DCSP_INIT_PARAM
{ {
std::string ModelPath; std::string ModelPath;
MODEL_TYPE ModelType = YOLO_ORIGIN_V8; MODEL_TYPE ModelType = YOLO_ORIGIN_V8;
std::vector<int> imgSize={640, 640}; std::vector<int> imgSize={640, 640};
int classesNum=80;
float RectConfidenceThreshold = 0.6; float RectConfidenceThreshold = 0.6;
float iouThreshold = 0.5; float iouThreshold = 0.5;
bool CudaEnable = false; bool CudaEnable = false;
@ -55,16 +56,14 @@ public:
public: public:
char* CreateSession(DCSP_INIT_PARAM &iParams); char* CreateSession(DCSP_INIT_PARAM &iParams);
char* RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT>& oResult); char* RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT>& oResult);
char* WarmUpSession(); char* WarmUpSession();
template<typename N> template<typename N>
char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult); char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult);
std::vector<std::string> classes{};
private: private:
Ort::Env env; Ort::Env env;
@ -74,8 +73,6 @@ private:
std::vector<const char*> inputNodeNames; std::vector<const char*> inputNodeNames;
std::vector<const char*> outputNodeNames; std::vector<const char*> outputNodeNames;
int classesNum;
MODEL_TYPE modelType; MODEL_TYPE modelType;
std::vector<int> imgSize; std::vector<int> imgSize;
float rectConfidenceThreshold; float rectConfidenceThreshold;

@ -1,44 +1,94 @@
#include <iostream> #include <iostream>
#include <stdio.h>
#include "inference.h" #include "inference.h"
#include <filesystem> #include <filesystem>
#include <fstream>
void file_iterator(DCSP_CORE*& p) void file_iterator(DCSP_CORE*& p)
{ {
std::filesystem::path img_path = R"(E:\project\Project_C++\DCPS_ONNX\TEST_ORIGIN)"; std::filesystem::path current_path = std::filesystem::current_path();
int k = 0; std::filesystem::path imgs_path = current_path/"images";
for (auto& i : std::filesystem::directory_iterator(img_path)) for (auto& i : std::filesystem::directory_iterator(imgs_path))
{ {
if (i.path().extension() == ".jpg") if (i.path().extension() == ".jpg" || i.path().extension() == ".png")
{ {
std::string img_path = i.path().string(); std::string img_path = i.path().string();
//std::cout << img_path << std::endl;
cv::Mat img = cv::imread(img_path); cv::Mat img = cv::imread(img_path);
std::vector<DCSP_RESULT> res; std::vector<DCSP_RESULT> res;
char* ret = p->RunSession(img, res); p->RunSession(img, res);
for (int i = 0; i < res.size(); i++)
for (auto & re : res)
{ {
cv::rectangle(img, res.at(i).box, cv::Scalar(125, 123, 0), 3); cv::rectangle(img, re.box, cv::Scalar(0, 0 , 255), 3);
std::string label = p->classes[re.classId];
cv::putText(
img,
label,
cv::Point(re.box.x, re.box.y - 5),
cv::FONT_HERSHEY_SIMPLEX,
0.75,
cv::Scalar(255, 255, 0),
2
);
} }
cv::imshow("Result", img);
k++;
cv::imshow("TEST_ORIGIN", img);
cv::waitKey(0); cv::waitKey(0);
cv::destroyAllWindows(); cv::destroyAllWindows();
//cv::imwrite("E:\\output\\" + std::to_string(k) + ".png", img);
} }
} }
} }
int read_coco_yaml(DCSP_CORE*& p)
{
// Open the YAML file
std::ifstream file("coco.yaml");
if (!file.is_open()) {
std::cerr << "Failed to open file" << std::endl;
return 1;
}
// Read the file line by line
std::string line;
std::vector<std::string> lines;
while (std::getline(file, line)) {
lines.push_back(line);
}
// Find the start and end of the names section
std::size_t start = 0;
std::size_t end = 0;
for (std::size_t i = 0; i < lines.size(); i++) {
if (lines[i].find("names:") != std::string::npos) {
start = i + 1;
} else if (start > 0 && lines[i].find(':') == std::string::npos) {
end = i;
break;
}
}
// Extract the names
std::vector<std::string> names;
for (std::size_t i = start; i < end; i++) {
std::stringstream ss(lines[i]);
std::string name;
std::getline(ss, name, ':'); // Extract the number before the delimiter
std::getline(ss, name); // Extract the string after the delimiter
names.push_back(name);
}
p->classes = names;
return 0;
}
int main() int main()
{ {
DCSP_CORE* p1 = new DCSP_CORE; DCSP_CORE* p1 = new DCSP_CORE;
std::string model_path = "yolov8n.onnx"; std::string model_path = "yolov8n.onnx";
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 80, 0.1, 0.5, false }; read_coco_yaml(p1);
char* ret = p1->CreateSession(params); // GPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, true };
// CPU inference
// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, false };
p1->CreateSession(params);
file_iterator(p1); file_iterator(p1);
} }

Loading…
Cancel
Save