AI学习笔记三十二：YOLOv8-CPP-Inference测试（Linux版本）

若该文为原创文章，转载请注明原文出处。

主要介绍如何在Linux系统上安装和部署基于YOLOv8的C++推理项目

一、服务器准备

使用AutoDL平台租用服务器

AutoDL有git加速，可以自行启用

二、环境配件

1、检查Opencv版本

pkg-config --modversion opencv4

如果版本为4.5，则需升级为4.9，否则ONNX模型在加载时可能报错

2、检查CMake版本

cmake --version

如果没有安装

sudo apt install cmake

三、安装Opencv 4.9

运行脚本

chmod +x install_opencv_490_fresh.sh
​
./install_opencv_490_fresh.sh

脚本：

#!/bin/bashecho "=== Step 1: Installing dependencies ==="
apt update
apt install -y build-essential cmake git pkg-config \libgtk-3-dev libavcodec-dev libavformat-dev libswscale-dev \libv4l-dev libxvidcore-dev libx264-dev libjpeg-dev libpng-dev \libtiff-dev gfortran openexr libatlas-base-dev python3-dev \python3-numpy libtbb-devecho "=== Step 2: Cloning OpenCV and opencv_contrib (version 4.9.0) ==="cd ~
rm -rf ~/opencv ~/opencv_contrib
git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.gitcd opencv
git checkout 4.9.0
cd ../opencv_contrib
git checkout 4.9.0echo "=== Step 3: Configuring build with pkg-config generation ==="
cd ~/opencv
rm -rf build
mkdir build
cd buildcmake -D CMAKE_BUILD_TYPE=Release \-D CMAKE_INSTALL_PREFIX=/usr/local \-D CMAKE_EXTRA_MODULES_PATH=~/opencv_contrib/modules \-D BUILD_EXAMPLES=OFF \-D WITH_CUDA=OFF \-D OPENCV_GENERATE_PKGCONFIG=ON \..if [ $? -ne 0 ]; thenecho "CMake failed! Check the error log."exit 1
fiecho "=== Step 4: Building OpenCV ==="
make -j$(nproc)echo "=== Step 5: Installing OpenCV ==="
make install
ldconfigecho "=== Step 6: Done! Verifying installation ==="
/usr/local/bin/opencv_version

​

编译时间很长，耐心等待

验证

pkg-config --modversion opencv4

四、YOLOV8模型导出

git clone https://github.com/ultralytics/ultralytics.git
​
cd ultralytics
​
pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple

导出ONNX模型（简化版本，兼容OpenCV DNN)

yolo export model=yolov8s.pt format=onnx opset=12 dynamic=False simplify=True

五、适配Linux无图形界面

将原来的显示代码替换为保存图片

替换前:

cv:imshow("Inference", frame);
​
cv::waitKey(-1);

替换后:

std::string outputPath = "output_" + std::to_string(i) + ".jpg";
​
cv::imwrite(outputPath, frame);
​
std::cout << "Saved result to: " << outputPath << std::endl;

六、修改文件

修改main.cpp文件，主要是修改路径和要不要启用GPU

七、CMakeLists.txt示例

# 设置最低CMake版本要求
cmake_minimum_required(VERSION 3.5)
# 项目名称和版本
project(Yolov8CPPInference VERSION 0.1)
#设置包含当前目录的头文件路径
set(CMAKE_INCLUDE_CURRENT_DIR ON)
​
#----------------- CUDA 设置 -----------------
# 设置 CUDA 路径（根据系统配置调整）
set(CUDA_TOOLKIT_ROOT_DIR "/usr/local/cuda")
# 查找并启用 CUDA (要求至少为CUDA 11)
find_package(CUDA 11 REQUIRED)
​
# 设置 CUDA 标准为 C++11
set(CMAKE_CUDA_STANDARD 11)
set(CMAKE_CUDA_STANDARD_REQUIRED ON)
# !CUDA
#-------------------------------------------
​
#----------------- OpenCV 设置 -----------------
# OpenCV
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
# !OpenCV
#-------------------------------------------
​
# 定义项目源文件
set(PROJECT_SOURCESmain.cpp
​inference.hinference.cpp
)
​
# 生成可执行文件
add_executable(Yolov8CPPInference ${PROJECT_SOURCES})
​
# 链接 OpenCV 库
target_link_libraries(Yolov8CPPInference ${OpenCV_LIBS})

​

八、构建与运行项目

# 创建构建项目
mkdir build
# 进入构建目录 
cd build
# 生成Makefile文件
cmake ..
# 编译项目
make
​
# 运行推理程序
./Yolov8CPPInference

​
​

后续想部署到自己的虚拟机上，并使用摄像头实时检测

如有侵权，或需要完整代码，请及时联系博主。