#include <iostream>
#include <fstream>
#include <iostream>
#include <chrono>
#include <string>
#include <io.h>
#include <vector>
#include <direct.h>
#include <math.h>
#include <sstream>
//OpenCV
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>//Kinect DK
#include <k4a/k4a.hpp>
#include <set>
// PCL 库
#include <pcl/io/pcd_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h>
#include <iostream>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/filters/crop_box.h>
#include <pcl/visualization/cloud_viewer.h>//定义点云类型
typedef pcl::PointXYZRGB PointT;
typedef pcl::PointCloud<PointT> PointCloud;using namespace cv;
using namespace std;std::set<int> getPixelIndicesInROI(const cv::Mat& image, const cv::Rect& roi) {std::set<int> indices;for (int x = roi.x; x < roi.x + roi.width; ++x){for (int y = roi.y; y < roi.y + roi.height; ++y){// 确保索引在图像尺寸范围内if (x >= 0 && y >= 0 && x < image.cols && y < image.rows) {indices.insert(x+ image.cols *y);}}}return indices;
}PointT point;
PointT point_center;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.camera_fps = K4A_FRAMES_PER_SECOND_15;config.color_format = K4A_IMAGE_FORMAT_COLOR_BGRA32;config.color_resolution = K4A_COLOR_RESOLUTION_720P;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 capturedevice.start_cameras(&config);std::cout << "Done: start camera." << std::endl;//写入txt文件流ofstream rgb_out;ofstream d_out;rgb_out.open("./rgb.txt");d_out.open("./depth.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;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;// 随机指定一个目标框,用于切割出点云（x,y,w,h）cv::Rect object1(451, 123, 210, 290);// 计算出中心点坐标float center_x = object1.x + object1.width / 2.0;float center_y = object1.y + object1.height / 2.0;int size_x = object1.width;int size_y = object1.height;// 目标框不行，必须用分割算法// 找出person的颜色区域// 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;float box_z = 0;int index = 0;while (index < 1) {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();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;uint8_t* color_buffer = rgbImage.get_buffer(); // 获取颜色图像数据的指针int width = rgbImage.get_width_pixels();int height = rgbImage.get_height_pixels();for (int i = 0; i < 10; i++) // 只打印前10个像素{int index = i * 4; // 每个像素占用4个字节（R、G、B 和 Alpha通道）std::cout << "Pixel[" << i << "]: ("<< (int)color_buffer[index] << ", " // R<< (int)color_buffer[index + 1] << ", " // G<< (int)color_buffer[index + 2] << ") " // B<< std::endl;}cv::Mat cv_rgbImage_with_alpha = cv::Mat(height, width, CV_8UC4, color_buffer, cv::Mat::AUTO_STEP);cv::Mat cv_image_no_alpha;cv::cvtColor(cv_rgbImage_with_alpha, cv_image_no_alpha, cv::COLOR_BGRA2BGR);std::set<int> roi_indices = getPixelIndicesInROI(cv_image_no_alpha, object1);cv::imwrite("a.jpg", cv_image_no_alpha);cv::imshow("color", cv_image_no_alpha);cv::waitKey(1);// 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();std::cout << "[depth] " << "\n"<< "format: " << depthImage.get_format() << "\n"<< "device_timestamp: " << depthImage.get_device_timestamp().count() << "\n"<< "system_timestamp: " << depthImage.get_system_timestamp().count() << "\n"<< "height*width: " << depthImage.get_height_pixels() << ", " << depthImage.get_width_pixels()<< std::endl;//获取彩色点云k4a::calibration k4aCalibration = device.get_calibration(config.depth_mode, config.color_resolution);float fx = k4aCalibration.color_camera_calibration.intrinsics.parameters.param.fx;float fy = k4aCalibration.color_camera_calibration.intrinsics.parameters.param.fy;std::cout << "fx: " << fx << "fy: " << fy << std::endl;k4a::transformation k4aTransformation = k4a::transformation(k4aCalibration);//PointCloud::Ptr cloud(new PointCloud);int color_image_width_pixels = rgbImage.get_width_pixels();int color_image_height_pixels = rgbImage.get_height_pixels();transformed_depthImage = NULL;transformed_depthImage = k4a::image::create(K4A_IMAGE_FORMAT_DEPTH16,color_image_width_pixels,color_image_height_pixels,color_image_width_pixels * (int)sizeof(uint16_t));k4a::image point_cloud_image = NULL;point_cloud_image = k4a::image::create(K4A_IMAGE_FORMAT_CUSTOM,color_image_width_pixels,color_image_height_pixels,color_image_width_pixels * 3 * (int)sizeof(int16_t));if (depthImage.get_width_pixels() == rgbImage.get_width_pixels() && depthImage.get_height_pixels() == rgbImage.get_height_pixels()) {std::copy(depthImage.get_buffer(), depthImage.get_buffer() + depthImage.get_height_pixels() * depthImage.get_width_pixels() * (int)sizeof(uint16_t), transformed_depthImage.get_buffer());}else {k4aTransformation.depth_image_to_color_camera(depthImage, &transformed_depthImage);}k4aTransformation.depth_image_to_point_cloud(transformed_depthImage, K4A_CALIBRATION_TYPE_COLOR, &point_cloud_image);pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);cloud->width = color_image_width_pixels;cloud->height = color_image_height_pixels;cloud->is_dense = false;cloud->resize(static_cast<size_t>(color_image_width_pixels) * color_image_height_pixels);const int16_t* point_cloud_image_data = reinterpret_cast<const int16_t*>(point_cloud_image.get_buffer());const uint8_t* color_image_data = rgbImage.get_buffer();std::set<float> object_pointx_list;std::set<float> object_pointy_list;std::set<float> object_pointz_list;for (int i = 0; i < color_image_width_pixels * color_image_height_pixels; i++) {if (roi_indices.count(i) > 0) {object_pointx_list.insert(point_cloud_image_data[3 * i + 0] / 1000.0f);object_pointy_list.insert(point_cloud_image_data[3 * i + 1] / 1000.0f);object_pointz_list.insert(point_cloud_image_data[3 * i + 2] / 1000.0f);if(i == int(center_x + center_y * width)){box_z = point_cloud_image_data[3 * i + 2] / 1000.0f;std::cout << "ddddd" <<box_z<< std::endl;}}point.x = point_cloud_image_data[3 * i + 0] / 1000.0f;point.y = point_cloud_image_data[3 * i + 1] / 1000.0f;point.z = point_cloud_image_data[3 * i + 2] / 1000.0f;point.b = color_image_data[4 * i + 0];point.g = color_image_data[4 * i + 1];point.r = color_image_data[4 * i + 2];uint8_t alpha = color_image_data[4 * i + 3];if (point.x == 0 && point.y == 0 && point.z == 0 && alpha == 0)continue;cloud->points[i] = point;}pcl::io::savePLYFile("4.ply", *cloud); //将点云数据保存为ply文件创建一个 PCLVisualizer 对象pcl::visualization::PCLVisualizer viewer("3D Viewer");创建表示框的模型系数pcl::ModelCoefficients coefficients;coefficients.values.resize(6); // 6个值分别表示 x_min, x_max, y_min, y_max, z_min, z_max定义框的最大和最小坐标coefficients.values[0] = *object_pointx_list.begin(); // x_mincoefficients.values[1] = *object_pointx_list.rbegin(); // x_maxcoefficients.values[2] = *object_pointy_list.begin(); // y_mincoefficients.values[3] = *object_pointy_list.rbegin(); // y_max// 暂时用固定的coefficients.values[4] = box_z-0.2; // z_mincoefficients.values[5] = box_z+0.1; // z_maxstd::cout << "x_min: " << coefficients.values[0] << "x_max: " << coefficients.values[1] << std::endl;std::cout << "y_min: " << coefficients.values[2] << "y_max: " << coefficients.values[3] << std::endl;std::cout << "z_min: " << coefficients.values[4] << "z_max: " << coefficients.values[5] << std::endl;pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(cloud);viewer.addPointCloud<pcl::PointXYZRGB>(cloud, rgb, "sample cloud");在可视化窗口中添加框viewer.addCube(coefficients.values[0], coefficients.values[1],coefficients.values[2], coefficients.values[3],coefficients.values[4], coefficients.values[5],1.0, 0.0, 0.0, "cube"); // 使用红色绘制框viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_REPRESENTATION, pcl::visualization::PCL_VISUALIZER_REPRESENTATION_WIREFRAME, "cube");viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, 2.0, "cube"); // 设置线宽循环直到关闭窗口while (!viewer.wasStopped()) {viewer.spinOnce(100);}}else {std::cout << "false: K4A_WAIT_RESULT_TIMEOUT." << std::endl;}index++;}cv::destroyAllWindows();rgb_out << flush;d_out << flush;rgb_out.close();d_out.close();// 释放，关闭设备rgbImage.reset();depthImage.reset();capture.reset();device.close();return 1;
}合成彩色点云数据步骤：
1、深度图像2D数据转为深度传感器下的3D点云数据
2、获得彩色传感器下的3D点云数据并投影到彩色图像
3、保存对应点的xyz坐标和rgb属性
4、遍历深度图像所有点，并排除数据异常点后得到的点集即为彩色点云数据
参考链接：
利用Azure Kinect 生成点云（C++)_azure kinect dk获取点云-CSDN博客
基于Kinect Azure的多相机数据采集（一）_kinect dk 两个相机融合-CSDN博客