使用C++代码保存深度相机D435i的RGB图片，包含C++代码以及CMakeLists.txt

1.任务

    需要标定深度相机，那么我们就需要拿到深度相机所拍摄的图片，灰度图或者是彩色图。以下提供C++代码，用于提取深度相机所拍摄的图片，每隔2秒保存一次RGB图像。

编译语言: C++
编译工具: cmake
系统版本: ubuntu 18.04
主要环境: librealsense2, opencv2
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2. 依赖环境安装

2.1 C++

    点我安装g++11.2与gdb
    点我安装cmake

2.2 librealsense2

    1）安装依赖包

sudo apt-get update
sudo apt-get upgrade
sudo apt-get dist-upgrade

sudo apt-get install libudev-dev pkg-config libgtk-3-dev
sudo apt-get install libusb-1.0-0-dev pkg-config
sudo apt-get install libglfw3-dev
sudo apt-get install libssl-dev
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    2）拉取源码

mkdir librealsense_install
cd librealsense_install
git clone -b v2.50.0 https://github.com/IntelRealSense/librealsense.git
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    3）安装权限脚本

cd librealsense
sudo cp config/99-realsense-libusb.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules && udevadm trigger 
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    4）编译

mkdir build
cd build
cmake ../ -DBUILD_EXAMPLES=true
make -j
sudo make install
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    5） 使用realsense 注:虚拟机usp设置大于3.0

realsense-viewer
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    6）卸载realsense

dpkg -l | grep "realsense" | cut -d " " -f 3 | xargs sudo dpkg --purge
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2.3 opencv2

    点我进入opencv2 安装

3.代码

CMakeLists.txt 文件

cmake_minimum_required(VERSION 3.12)

project(video)
set(CMAKE_BUILD_TYPE Debug)
set(CMAKE_CXX_FLAGS "-std=c++17")

# 查找OpenCV包
find_package(OpenCV REQUIRED)
# 寻找Intel RealSense SDK包
find_package(realsense2 REQUIRED)
find_package(Threads REQUIRED)

# 添加可执行文件
add_executable(video video.cpp)

target_compile_features(video PRIVATE cxx_std_17)

# 链接OpenCV库
target_link_libraries(video ${OpenCV_LIBS})
target_link_libraries(video realsense2)
target_link_libraries(video Threads::Threads)
target_link_libraries(video -lstdc++fs)
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video.cpp 代码文件

#include 
#include 
#include 
#include 

// 定义函数将RS2帧转换为OpenCV Mat
cv::Mat frame_to_mat(const rs2::frame& frame) {
    rs2::video_frame video_frame = frame.as<rs2::video_frame>();
    const int width = video_frame.get_width();
    const int height = video_frame.get_height();
    const int bpp = video_frame.get_bytes_per_pixel();
    return cv::Mat(cv::Size(width, height), CV_8UC(bpp), (void*)video_frame.get_data(), cv::Mat::AUTO_STEP);
}

int main() {
    rs2::pipeline pipe;
    rs2::config cfg;
    cfg.enable_stream(RS2_STREAM_COLOR, 640, 480, RS2_FORMAT_BGR8, 30);
//    cfg.enable_stream(RS2_STREAM_COLOR);

    //    cfg.enable_stream(RS2_STREAM_DEPTH, 640, 480, RS2_FORMAT_Z16, 30);

    // 启动管道
    pipe.start(cfg);

    int count = 0;
    while (true) {
        // 等待深度相机捕捉到帧
        rs2::frameset frameset = pipe.wait_for_frames();
        auto color_frame = frameset.get_color_frame();
//        auto depth_frame = frameset.get_depth_frame();

        // 将RS2帧转换为OpenCV Mat
        cv::Mat color_mat = frame_to_mat(color_frame);
//        cv::Mat depth_mat = frame_to_mat(depth_frame); // 添加深度图像处理

        std::cout << "Color image channels: " << color_mat.channels() << std::endl;
//        std::cout << "Depth image channels: " << depth_mat.channels() << std::endl;

        // 保存RGB图像和深度图像
        std::string color_filename = "color_image" + std::to_string(count) + ".png";
//        std::string depth_filename = "depth_image" + std::to_string(count) + ".png"; // 深度图像文件名

        cv::imwrite(color_filename, color_mat);
//        cv::imwrite(depth_filename, depth_mat); // 保存深度图像

//        std::cout << "Saved " << color_filename << " and " << depth_filename << std::endl;
        std::cout << "Saved " << color_filename << std::endl;

        // 每隔2秒保存一次图片
        std::this_thread::sleep_for(std::chrono::seconds(2));
        count++;
    }

    pipe.stop();

    return 0;
}

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4.成功运行

    保存图片成功的截图