mac安装opencv并在vscode中配置c++环境调试推理YOLOv8网络模型

OpenCV（Open Source Computer Vision Library）是一个开源的计算机视觉库，提供了丰富的图像处理和计算机视觉算法。它支持多平台（包括 Windows、Linux、macOS）和多种编程语言（如 C++、Python、Java），使其成为研究、开发和部署计算机视觉应用的重要工具之一。

步骤 1: 创建项目目录和必要的文件

创建项目目录并进入：

mkdir yolov8_project
cd yolov8_project
mkdir build

创建 CMakeLists.txt 文件并添加内容：

touch CMakeLists.txt

内容如下：

cmake_minimum_required(VERSION 3.10)
project(yolov8)set(CMAKE_CXX_STANDARD 14)
# Set OpenCV_DIR to the directory where OpenCVConfig.cmake is located
set(OpenCV_DIR /usr/local/opt/opencv/lib/cmake/opencv4)# Find OpenCV package
find_package(OpenCV REQUIRED)# Include OpenCV directories
include_directories(${OpenCV_INCLUDE_DIRS})# Link OpenCV libraries
add_executable(yolov8 yolov8.cpp)
target_link_libraries(yolov8 ${OpenCV_LIBS})# Print OpenCV include directories and libraries
message(STATUS "OpenCV include directories: ${OpenCV_INCLUDE_DIRS}")
message(STATUS "OpenCV libraries: ${OpenCV_LIBS}")

创建 yolov8.cpp 文件并添加您的代码：

touch yolov8.cpp

内容如下：

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <fstream>using namespace std;
using namespace cv;struct DCSP_RESULT {int classId;float confidence;cv::Rect box;
};void process(cv::Mat& blob, cv::dnn::Net& net, std::vector<cv::Mat>& outputs) {net.setInput(blob);net.forward(outputs, net.getUnconnectedOutLayersNames());
}void pre_process(cv::Mat& image, cv::Mat& blob) {cv::dnn::blobFromImage(image, blob, 1. / 255., cv::Size(640, 640), cv::Scalar(0, 0, 0), true, false);
}void saveTensor(const std::string& filename, const cv::Mat& tensor) {std::ofstream file(filename, std::ios::binary);if (file.is_open()) {file.write(reinterpret_cast<const char*>(tensor.data), tensor.total() * tensor.elemSize());file.close();}
}void post_process(cv::Mat& image, const std::vector<cv::Mat>& outs, float confThreshold,</