基于Azure Kinect DK相机的安装配置，获取并保存RGB、Depth、IR图、点云，点云融合（Windows）

版本：VS2019、Kinect3.0、相机传感器SDK1.4.0、OpenCV3.4.1、PCL1.10.1
参考文章：
基于Azure Kinect SDK获取物体rgb图、深度图、红外IR图和点云数据并保存到本地
RGB-D相机（Azure Kinect DK）RGB图、深度图的获取，配准与保存

项目配置

OpenCV和PCL安装

VS2019配置PCL（windows）
VS2019配置OpenCV（windows）

相机安装

1、将相机连接电脑
2、下载并安装传感器SDK
下载地址：https://github.com/microsoft/Azure-Kinect-Sensor-SDK/blob/develop/docs/usage.md

下载完成后点击安装即可

配置到VS项目中

1、选择项目中的引用，右键选择管理NuGet包

2、在浏览中，选择下图所示包，点击下载安装即可


3、在项目中添加头文件

#include <k4a/k4a.h>
#include <k4arecord/record.h>
#include <k4arecord/playback.h>

获取RGB、Depth、IR图并保存

需要在脚本目录下创建rgb、depth、ir三个文件夹才可以保存图片

代码

// C++
#include <iostream>
#include <chrono>
#include <string>
// OpenCV
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
// Kinect DK
#include <k4a/k4a.hpp>

using namespace cv;
using namespace std;

// 宏
// 方便控制是否 std::cout 信息
#define DEBUG_std_cout 0

int main(int argc, char* argv[]) {
   
    /*
        找到并打开 Azure Kinect 设备
    */
    // 发现已连接的设备数

    const uint32_t device_count = k4a::device::get_installed_count();
    if (0 == device_count) {
   
        std::cout << "Error: no K4A devices found. " << std::endl;
        return -1;
    }
    else {
   
        std::cout << "Found " << device_count << " connected devices. " << std::endl;
        if (1 != device_count)// 超过1个设备，也输出错误信息。
        {
   
            std::cout << "Error: more than one K4A devices found. " << std::endl;
            return -1;
        }
        else// 该示例代码仅限对1个设备操作
        {
   
            std::cout << "Done: found 1 K4A device. " << std::endl;
        }
    }
    // 打开（默认）设备
    k4a::device device = k4a::device::open(K4A_DEVICE_DEFAULT);
    std::cout << "Done: open device. " << std::endl;

    /*
        检索并保存 Azure Kinect 图像数据
    */
    // 配置并启动设备
    k4a_device_configuration_t config = K4A_DEVICE_CONFIG_INIT_DISABLE_ALL;
    config.camera_fps = K4A_FRAMES_PER_SECOND_30;
    config.color_format = K4A_IMAGE_FORMAT_COLOR_BGRA32;
    config.color_resolution = K4A_COLOR_RESOLUTION_1080P;
    config.depth_mode = K4A_DEPTH_MODE_NFOV_UNBINNED;
    //config.depth_mode = K4A_DEPTH_MODE_WFOV_2X2BINNED;
    config.synchronized_images_only = true;// ensures that depth and color images are both available in the capture
    device.start_cameras(&config);
    std::cout << "Done: start camera." << std::endl;

    //写入txt文件流
    ofstream rgb_out;
    ofstream d_out;
    ofstream ir_out;

    rgb_out.open("./rgb.txt");
    d_out.open("./depth.txt");
    ir_out.open("./ir.txt");

    rgb_out << "#  color images" << endl;
    rgb_out << "#  file: rgbd_dataset" << endl;
    rgb_out << "#  timestamp" << "    " << "filename" << endl;

    d_out << "#  depth images" << endl;
    d_out << "#  file: rgbd_dataset" << endl;
    d_out << "#  timestamp" << "    " << "filename" << endl;

    ir_out << "#  ir images" << endl;
    ir_out << "#  file: rgbd_dataset" << endl;
    ir_out << "#  timestamp" << "    " << "filename" << endl;

    rgb_out << flush;
    d_out << flush;
    // 稳定化
    k4a::capture capture;
    int iAuto = 0;//用来稳定，类似自动曝光
    int iAutoError = 0;// 统计自动曝光的失败次数
    while (true) {
   
        if (device.get_capture(&capture)) {
   
            std::cout << iAuto << ". Capture several frames to give auto-exposure" << std::endl;

            // 跳过前 n 个（成功的数据采集）循环，用来稳定
            if (iAuto != 30) {
   
                iAuto++;
                continue;
            }
            else {
   
                std::cout << "Done: auto-exposure" << std::endl;
                break;// 跳出该循环，完成相机的稳定过程
            }

        }
        else {
   
            std::cout << iAutoError << ". K4A_WAIT_RESULT_TIMEOUT." << std::endl;
            if (iAutoError != 30) {
   
                iAutoError++;
                continue;
            }
            else {
   
                std::cout << "Error: failed to give auto-exposure. " << std::endl;
                return -1;
            }
        }
    }
    std::cout << "-----------------------------------" << std::endl;
    std::cout << "----- Have Started Kinect DK. -----" << std::endl;
    std::cout << "-----------------------------------" << std::endl;
    // 从设备获取捕获
    k4a::image rgbImage;
    k4a::image depthImage;
    k4a::image irImage;
    k4a::image transformed_depthImage;

    cv::Mat cv_rgbImage_with_alpha;
    cv::Mat cv_rgbImage_no_alpha;
    cv::Mat cv_depth;
    cv::Mat cv_depth_8U;
    cv::Mat cv_irImage;
    cv::Mat cv_irImage_8U;

    while (true)
        // for (size_t i = 0; i < 100; i++)
    {
   
        // if (device.get_capture(&capture, std::chrono::milliseconds(0)))
        if (device.get_capture(&capture)) {
   
            // rgb
            // * Each pixel of BGRA32 data is four bytes. The first three bytes represent Blue, Green,
            // * and Red data. The fourth byte is the alpha channel and is unused in the Azure Kinect APIs.
            rgbImage = capture.get_color_image();
#if DEBUG_std_cout == 1
            std::cout << "[rgb] " << "\n"
                << "format: " << rgbImage.get_format() << "\n"
                << "device_timestamp: " << rgbImage.get_device_timestamp().count() << "\n"
                << "system_timestamp: " << rgbImage.get_system_timestamp().count() << "\n"
                << "height*width: " << rgbImage.get_height_pixels() << ", " << rgbImage.get_width_pixels()
                << std::endl;
#endif

            // depth
            // * Each pixel of DEPTH16 data is two bytes of little endian unsigned depth data. The unit of the data is in
            // * millimeters from the origin of the camera.
            depthImage = capture.get_depth_image();
#if DEBUG_std_cout == 1
            std::cout << "[depth] " << "\n"
                << "format: " << depthImage.get_format() << "\n"
                &