基于OpenCV和KinectSDK实现Azure Kinect DK实时获取RGB、深度图像及红外图像、交互式数据采集将图片数据保存本地

Robot_Tiger_

发布时间 2021.11.18阅读数 8168 评论数 0

基于OpenCV和KinectSDK实现Azure Kinect DK实时获取RGB、深度图像及红外图像、交互式数据采集将图片数据保存本地

基于OpenCV和KinectSDK实现Azure Kinect DK实时获取RGB、深度图像及红外图像、交互式数据采集将图片数据保存本地
1.开发环境
2.采集效果图
- 2.1采集界面
- 2.2RGB图像
- 2.3 深度图像
- 2.4红外图像
3.深度图计算原理
4.代码主要函数及其功能
- 4.1配置并启动Kinect设备
- 4.2声明数据结构
- 4.3创建txt文件流
- 4.4 循环获取每一帧的图像
- 4.5数据格式转换
- 4.6通过捕获键盘进行交互式数据采集
5.完整代码
- 5.1头文件ShowDep_Color.h
- 5.2ShowDep_Color.cpp

1.开发环境

-Visual Studio2017
-Microsoft.Azure.Kinect.Sensor1.4.1
-OpenCV3.4.1
-关于VS2017配置OpenCV环境的详细过程可以参考我的上一篇文章：
链接: VS2017逐步配置OpenCV教程

2.采集效果图

2.1采集界面

先上实际采集效果图，可以通过自定义按键控制其在Kinect视频流中交互式保存某一帧的彩色图、深度图、红外图数据，并将深度图的深度数据保存在ply文件中便于后续将开发。

在这里插入图片描述

2.2RGB图像

在这里插入图片描述

2.3 深度图像

在这里插入图片描述

2.4红外图像

在这里插入图片描述

3.深度图计算原理

这里列出几位大佬的文章，对于深度图的原理及计算方法讲解的非常详细：
链接1: 深度图像的获取原理
链接2: 有趣的深度图：可见性问题的解法

4.代码主要函数及其功能

4.1配置并启动Kinect设备

//这一代 Kinect 支持在一台电脑上打开多台 Kinect。可以用这句代码通过查询主机已连接的Kinet设备数来判断这台主机是否连接了 Kinect
	const uint32_t deviceCount = k4a::device::get_installed_count();
	if (deviceCount == 0)
	{
		cout << "未连接任何Kinect设备！" << endl;
	}

	//配置并启动设备
	k4a_device_configuration_t config = K4A_DEVICE_CONFIG_INIT_DISABLE_ALL;
	//设置Kinect的相机帧率为30FPS
	config.camera_fps = K4A_FRAMES_PER_SECOND_30;
	//设置Kinect的深度模式为Near FOV unbinned（这一代 Kinect 支持多种深度模式，官方文档推荐使用 K4A_DEPTH_MODE_NFOV_UNBINNED 和 K4A_DEPTH_MODE_WFOV_2X2BINNED 这两种深度模式）
	config.depth_mode = K4A_DEPTH_MODE_NFOV_UNBINNED;
	//设置Kinect的颜色属性为BGRA32
	config.color_format = K4A_IMAGE_FORMAT_COLOR_BGRA32;
	//设置color图的分辨率位 720p
	config.color_resolution = K4A_COLOR_RESOLUTION_720P;
	//为了同时获取depth和color图，保持这两种图像是同步的
	config.synchronized_images_only = true;

	cout << "Started opening K4A device..." << endl;
	k4a::device device = k4a::device::open(K4A_DEVICE_DEFAULT);
	device.start_cameras(&config);
	cout << "Finished opening K4A device." << endl;

4.2声明数据结构

Kinect 表示图像(RGB / IR / Depth)的内置数据结构 k4a::image。同时使用 cv::Mat数据结构，以便利用 OpenCV 的imshow函数来实时示。

	k4a::image depthImage;
	k4a::image rgbImage;
	k4a::image irImage;
	k4a::image transformed_depthImage;

	cv::Mat cv_rgbImage_with_alpha;
	cv::Mat cv_rgbImage_no_alpha;
	cv::Mat cv_rgbImage_8U;
	cv::Mat cv_depth;
	cv::Mat cv_depth_8U;
	cv::Mat cv_irImage;
	cv::Mat cv_irImage_8U;

	//声明一个Kinect的capture
	k4a::capture capture;

	cv::Mat depthFrame;
	cv::Mat rgbFrame;
	cv::Mat irFrame;

4.3创建txt文件流

通过txt文件来存储已获取的图片数据的数量及名称

// An highlighted block
//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;

4.4 循环获取每一帧的图像

使用 dev.get_capture 函数得到 capture 对象，并设置一个 blocking 时间，表示每隔一个 blocking 时间就获取一次数据，如果这个时间设置为 0 ，表示不需要间隔时间，来一帧获取一帧，并通过相对应的函数来得到相对应格式的图像数据。

while (1)
{
  if (dev.get_capture(&capture, std::chrono::milliseconds(0)))
  {
      k4a::image depthImage = capture.get_depth_image();
      k4a::image colorImage = capture.get_color_image();
      k4a::image irImage = capture.get_ir_image();
  }
}

4.5数据格式转换

ColorizeDepthImage(depthImage,DepthPixelColorizer::ColorizeBlueToRed,GetDepthModeRange(config.depth_mode), &depthTextureBuffer);
ColorizeDepthImage(irImage,DepthPixelColorizer::ColorizeGreyscale,GetIrLevels(K4A_DEPTH_MODE_PASSIVE_IR), &irTextureBuffer);
colorTextureBuffer = rgbImage.get_buffer();
//从设备中获取每一帧的彩色图、深度图及红外图数据
depthFrame = cv::Mat(depthImage.get_height_pixels(), depthImage.get_width_pixels(), CV_8UC4, depthTextureBuffer.data());
rgbFrame = cv::Mat(rgbImage.get_height_pixels(), rgbImage.get_width_pixels(), CV_8UC4, colorTextureBuffer);
irFrame = cv::Mat(irImage.get_height_pixels(), irImage.get_width_pixels(), CV_8UC4, irTextureBuffer.data());
//将k4A定义的数据格式转换为OpenCV可显示的数据格式
depthFrame.convertTo(cv_depth_8U, CV_8U, 1);
rgbFrame.convertTo(cv_rgbImage_8U, CV_8U, 1);
irFrame.convertTo(cv_irImage_8U, CV_8U, 1);
//根据需求显示相应的输出窗口
//cv::imshow("Kinect depth map", depthFrame); 
cv::imshow("Kinect color frame", rgbFrame);
//cv::imshow("Kinect color frame", cv_rgbImage_8U);
//cv::imshow("Kinect ir frame", irFrame);

4.6通过捕获键盘进行交互式数据采集

if (_kbhit()) {//如果有按键按下，则_kbhit()函数返回真
	imshow("Kinect color frame", rgbFrame);
	ch = _getch();//使用_getch()函数获取按下的键值
	cout << "-----采集成功！-----" << endl;
	cout << "                   " << endl;
	//按下回车键时开始采集图像
	if (ch == 13)
	{
		//深度图和RGB图配准
		//Get the camera calibration for the entire K4A device, which is used for all transformation functions.
		k4a::calibration k4aCalibration = device.get_calibration(config.depth_mode, config.color_resolution);

		k4a::transformation k4aTransformation = k4a::transformation(k4aCalibration);
		transformed_depthImage =k4aTransformation.depth_image_to_color_camera(depthImage);

		cv_rgbImage_with_alphacv::Mat(rgbImage.get_height_pixels(),rgbImage.get_width_pixels(), CV_8UC4,(void *)rgbImage.get_buffer());
		cv::cvtColor(cv_rgbImage_with_alpha, cv_rgbImage_no_alpha, cv::COLOR_BGRA2BGR);

		cv_depth = cv::Mat(transformed_depthImage.get_height_pixels(), transformed_depthImage.get_width_pixels(), CV_16U,(void *)transformed_depthImage.get_buffer(), static_cast<size_t>(transformed_depthImage.get_stride_bytes()));

		normalize(cv_depth, cv_depth_8U, 0, 256 * 256, NORM_MINMAX);
		cv_depth_8U.convertTo(cv_depth, CV_8U, 1);

		cv_irImage = cv::Mat(irImage.get_height_pixels(), irImage.get_width_pixels(), CV_16U,(void *)irImage.get_buffer(), static_cast<size_t>(irImage.get_stride_bytes()));
		normalize(cv_irImage, cv_irImage_8U, 0, 256 * 256, NORM_MINMAX);
		cv_irImage.convertTo(cv_irImage_8U, CV_8U, 1);	
		//save image
		double time_rgb = static_cast<double(std::chrono::duration_cast<std::chrono::microseconds>(
		rgbImage.get_device_timestamp()).count());
		std::string filename_rgb = std::to_string(time_rgb / 1000000) + ".png";

		double time_d = static_cast<double>(std::chrono::duration_cast<std::chrono::microseconds>(
		depthImage.get_device_timestamp()).count());
		std::string filename_d = std::to_string(time_d / 1000000) + ".png";
		
		double time_ir = static_cast<double>(std::chrono::duration_cast<std::chrono::microseconds>(
		irImage.get_device_timestamp()).count());
		std::string filename_ir = std::to_string(time_ir / 1000000) + ".png";
					
		imwrite("./RGB/" + filename_rgb, cv_rgbImage_8U);
		imwrite("./Depth/" + filename_d, cv_depth_8U);
		imwrite("./Ir/" + filename_ir, cv_irImage_8U);

		k4a_image_t xy_table = NULL;
		k4a_image_t point_cloud = NULL;
		int point_count = 0;

		double time_point = static_cast<double>(std::chrono::duration_cast<std::chrono::microseconds>(
		rgbImage.get_device_timestamp()).count());
		std::string filename_point = "ply/" + std::to_string(time_point / 1000000) + ".ply";
		k4a_image_create(K4A_IMAGE_FORMAT_CUSTOM,
		k4aCalibration.depth_camera_calibration.resolution_width,
		k4aCalibration.depth_camera_calibration.resolution_height,
		k4aCalibration.depth_camera_calibration.resolution_width * (int)sizeof(k4a_float2_t),&xy_table);

		create_xy_table(&k4aCalibration, xy_table);

		k4a_image_create(K4A_IMAGE_FORMAT_CUSTOM,
						 k4aCalibration.depth_camera_calibration.resolution_width,
					  	 k4aCalibration.depth_camera_calibration.resolution_height,
						 k4aCalibration.depth_camera_calibration.resolution_width*(int)sizeof(k4a_float3_t),&point_cloud);

		if (depthImage == 0)
		{
			printf("Failed to get depth image from capture\n");
		}

		generate_point_cloud(depthImage, xy_table, point_cloud, &point_count);

		write_point_cloud(filename_point.c_str(), point_cloud, point_count);

		k4a_image_release(xy_table);
		k4a_image_release(point_cloud);
		std::cout << "Acquiring!" << endl;

		//写入depth.txt, rgb.txt文件
		rgb_out << std::to_string(time_rgb / 1000000) << "    " << "rgb/" << filename_rgb << endl;
		d_out << std::to_string(time_d / 1000000) << "    " << "depth/" << filename_d << endl;
		ir_out << std::to_string(time_ir / 1000000) << "    " << "ir/" << filename_ir << endl;

		imshow("Kinect color frame", rgbFrame);
	}
}
if (waitKey(30) == 27 || waitKey(30) == 'q')
{
	device.close();
	std::cout << "----------------------------------" << std::endl;
	std::cout << "------------- closed -------------" << std::endl;
	std::cout << "----------------------------------" << std::endl;
	break;
}

5.完整代码

5.1头文件ShowDep_Color.h

#pragma once
#pragma once
#include <utility>
#include <k4a/k4a.hpp>

#include "Pixel.h"


#include<iostream>
#include<vector>
#include<array>
#include <fstream>
#include <chrono>
#include <string>
#include <math.h>
#include <sstream>

#include<k4a/k4a.hpp>	
#include<k4a/k4atypes.h>	

#include <opencv2/opencv.hpp>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>

#include<pcl/io/pcd_io.h>
#include<pcl/point_cloud.h>
#include<pcl/point_types.h>
#include<pcl/visualization/pcl_visualizer.h>

#include "Pixel.h"
#include "DepthPixelColorizer.h"
#include "StaticImageProperties.h"
#include "k4a_grabber.h"

#include <conio.h>

#pragma comment(lib, "User32.lib")
#pragma comment(lib, "gdi32.lib")

using namespace std;
using namespace cv;
using namespace sen;

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

namespace Show
{
	void ShowDep_Color();
	void create_xy_table(const k4a_calibration_t *calibration, k4a_image_t xy_table);
	void generate_point_cloud(const k4a::image depth_image, const k4a_image_t xy_table, k4a_image_t point_cloud, int *point_count);
	void write_point_cloud(const char *file_name, const k4a_image_t point_cloud, int point_count);
}

5.2ShowDep_Color.cpp

#include "ShowDep_Color.h"


void Show::create_xy_table(const k4a_calibration_t *calibration, k4a_image_t xy_table)
{
	k4a_float2_t *table_data = (k4a_float2_t *)(void *)k4a_image_get_buffer(xy_table);

	int width = calibration->depth_camera_calibration.resolution_width;
	int height = calibration->depth_camera_calibration.resolution_height;

	k4a_float2_t p;
	k4a_float3_t ray;
	int valid;

	for (int y = 0, idx = 0; y < height; y++)
	{
		p.xy.y = (float)y;
		for (int x = 0; x < width; x++, idx++)
		{
			p.xy.x = (float)x;

			k4a_calibration_2d_to_3d(
				calibration, &p, 1.f, K4A_CALIBRATION_TYPE_DEPTH, K4A_CALIBRATION_TYPE_DEPTH, &ray, &valid);

			if (valid)
			{
				table_data[idx].xy.x = ray.xyz.x;
				table_data[idx].xy.y = ray.xyz.y;
			}
			else
			{
				table_data[idx].xy.x = nanf("");
				table_data[idx].xy.y = nanf("");
			}
		}
	}
}

void Show::generate_point_cloud(const k4a::image depth_image, const k4a_image_t xy_table, k4a_image_t point_cloud, int *point_count)
{
	int width = depth_image.get_width_pixels();
	int height = depth_image.get_height_pixels();
	//int height = k4a_image_get_height_pixels(depth_image);

	uint16_t *depth_data = (uint16_t *)(void *)depth_image.get_buffer();
	k4a_float2_t *xy_table_data = (k4a_float2_t *)(void *)k4a_image_get_buffer(xy_table);
	k4a_float3_t *point_cloud_data = (k4a_float3_t *)(void *)k4a_image_get_buffer(point_cloud);

	*point_count = 0;
	for (int i = 0; i < width * height; i++)
	{
		if (depth_data[i] != 0 && !isnan(xy_table_data[i].xy.x) && !isnan(xy_table_data[i].xy.y))
		{
			point_cloud_data[i].xyz.x = xy_table_data[i].xy.x * (float)depth_data[i];
			point_cloud_data[i].xyz.y = xy_table_data[i].xy.y * (float)depth_data[i];
			point_cloud_data[i].xyz.z = (float)depth_data[i];
			(*point_count)++;
		}
		else
		{
			point_cloud_data[i].xyz.x = nanf("");
			point_cloud_data[i].xyz.y = nanf("");
			point_cloud_data[i].xyz.z = nanf("");
		}
	}
}

void Show::write_point_cloud(const char *file_name, const k4a_image_t point_cloud, int point_count)
{
	int width = k4a_image_get_width_pixels(point_cloud);
	int height = k4a_image_get_height_pixels(point_cloud);

	k4a_float3_t *point_cloud_data = (k4a_float3_t *)(void *)k4a_image_get_buffer(point_cloud);

	//save to the ply file
	std::ofstream ofs(file_name); // text mode first
	ofs << "ply" << std::endl;
	ofs << "format ascii 1.0" << std::endl;
	ofs << "element vertex"
		<< " " << point_count << std::endl;
	ofs << "property float x" << std::endl;
	ofs << "property float y" << std::endl;
	ofs << "property float z" << std::endl;
	ofs << "end_header" << std::endl;
	ofs.close();

	std::stringstream ss;
	for (int i = 0; i < width * height; i++)
	{
		if (isnan(point_cloud_data[i].xyz.x) || isnan(point_cloud_data[i].xyz.y) || isnan(point_cloud_data[i].xyz.z))
		{
			continue;
		}

		ss << (float)point_cloud_data[i].xyz.x << " " << (float)point_cloud_data[i].xyz.y << " "
			<< (float)point_cloud_data[i].xyz.z << std::endl;
	}

	std::ofstream ofs_text(file_name, std::ios::out | std::ios::app);
	ofs_text.write(ss.str().c_str(), (std::streamsize)ss.str().length());
}



void Show::ShowDep_Color(){
	//这一代 Kinect 支持在一台电脑上打开多台 Kinect。可以用这句代码通过查询主机已连接的Kinet设备数来判断这台主机是否连接了 Kinect
	const uint32_t deviceCount = k4a::device::get_installed_count();
	if (deviceCount == 0)
	{
		cout << "未连接任何Kinect设备！" << endl;
	}

	//配置并启动设备
	k4a_device_configuration_t config = K4A_DEVICE_CONFIG_INIT_DISABLE_ALL;
	//设置Kinect的相机帧率为30FPS
	config.camera_fps = K4A_FRAMES_PER_SECOND_30;
	//设置Kinect的深度模式为Near FOV unbinned（这一代 Kinect 支持多种深度模式，官方文档推荐使用 K4A_DEPTH_MODE_NFOV_UNBINNED 和 K4A_DEPTH_MODE_WFOV_2X2BINNED 这两种深度模式）
	config.depth_mode = K4A_DEPTH_MODE_NFOV_UNBINNED;
	//设置Kinect的颜色属性为BGRA32
	config.color_format = K4A_IMAGE_FORMAT_COLOR_BGRA32;
	//设置color图的分辨率位 720p
	config.color_resolution = K4A_COLOR_RESOLUTION_720P;
	//为了同时获取depth和color图，保持这两种图像是同步的
	config.synchronized_images_only = true;

	cout << "Started opening K4A device..." << endl;
	k4a::device device = k4a::device::open(K4A_DEVICE_DEFAULT);
	device.start_cameras(&config);
	cout << "Finished opening K4A device." << endl;

	std::vector<Pixel> depthTextureBuffer;
	std::vector<Pixel> irTextureBuffer;
	uint8_t *colorTextureBuffer;

	//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;

	//从设备获取捕获图像
	k4a::image depthImage;
	k4a::image rgbImage;
	k4a::image irImage;
	k4a::image transformed_depthImage;

	cv::Mat cv_rgbImage_with_alpha;
	cv::Mat cv_rgbImage_no_alpha;
	cv::Mat cv_rgbImage_8U;
	cv::Mat cv_depth;
	cv::Mat cv_depth_8U;
	cv::Mat cv_irImage;
	cv::Mat cv_irImage_8U;

	//声明一个Kinect的capture
	k4a::capture capture;

	cv::Mat depthFrame;
	cv::Mat rgbFrame;
	cv::Mat irFrame;
	int ch;
	/*
		检索并保存 Azure Kinect 图像数据
	*/	
	k4a_device_t device_Record = NULL;
	while (1) {
		if (device.get_capture(&capture, std::chrono::milliseconds(0)))
		{
			//通过对应的函数获得相应格式的图像数据
			depthImage = capture.get_depth_image();
			rgbImage = capture.get_color_image();
			irImage = capture.get_ir_image();

			ColorizeDepthImage(depthImage, DepthPixelColorizer::ColorizeBlueToRed, GetDepthModeRange(config.depth_mode), &depthTextureBuffer);
			ColorizeDepthImage(irImage, DepthPixelColorizer::ColorizeGreyscale, GetIrLevels(K4A_DEPTH_MODE_PASSIVE_IR), &irTextureBuffer);
			colorTextureBuffer = rgbImage.get_buffer();

			depthFrame = cv::Mat(depthImage.get_height_pixels(), depthImage.get_width_pixels(), CV_8UC4, depthTextureBuffer.data());
			rgbFrame = cv::Mat(rgbImage.get_height_pixels(), rgbImage.get_width_pixels(), CV_8UC4, colorTextureBuffer);
			irFrame = cv::Mat(irImage.get_height_pixels(), irImage.get_width_pixels(), CV_8UC4, irTextureBuffer.data());

			depthFrame.convertTo(cv_depth_8U, CV_8U, 1);
			rgbFrame.convertTo(cv_rgbImage_8U, CV_8U, 1);
			irFrame.convertTo(cv_irImage_8U, CV_8U, 1);

			//imshow("Kinect depth map", depthFrame); 
			cv::imshow("Kinect color frame", rgbFrame);
			//imshow("Kinect color frame", cv_rgbImage_8U);
			//imshow("Kinect ir frame", irFrame);
			
			if (_kbhit()) {//如果有按键按下，则_kbhit()函数返回真
				imshow("Kinect color frame", rgbFrame);
				ch = _getch();//使用_getch()函数获取按下的键值
				cout << "-----采集成功！-----" << endl;
				cout << "                   " << endl;
				//按下回车键时开始采集图像
				if (ch == 13)
				{
					//深度图和RGB图配准
						//Get the camera calibration for the entire K4A device, which is used for all transformation functions.
					k4a::calibration k4aCalibration = device.get_calibration(config.depth_mode, config.color_resolution);

					k4a::transformation k4aTransformation = k4a::transformation(k4aCalibration);

					transformed_depthImage = k4aTransformation.depth_image_to_color_camera(depthImage);

					cv_rgbImage_with_alpha = cv::Mat(rgbImage.get_height_pixels(), rgbImage.get_width_pixels(), CV_8UC4,
						(void *)rgbImage.get_buffer());
					cv::cvtColor(cv_rgbImage_with_alpha, cv_rgbImage_no_alpha, cv::COLOR_BGRA2BGR);

					cv_depth = cv::Mat(transformed_depthImage.get_height_pixels(), transformed_depthImage.get_width_pixels(), CV_16U,
						(void *)transformed_depthImage.get_buffer(), static_cast<size_t>(transformed_depthImage.get_stride_bytes()));

					normalize(cv_depth, cv_depth_8U, 0, 256 * 256, NORM_MINMAX);
					cv_depth_8U.convertTo(cv_depth, CV_8U, 1);

					cv_irImage = cv::Mat(irImage.get_height_pixels(), irImage.get_width_pixels(), CV_16U,
						(void *)irImage.get_buffer(), static_cast<size_t>(irImage.get_stride_bytes()));
					normalize(cv_irImage, cv_irImage_8U, 0, 256 * 256, NORM_MINMAX);
					cv_irImage.convertTo(cv_irImage_8U, CV_8U, 1);

					//imshow("Kinect color frame", rgbFrame); 
					//save image
					double time_rgb = static_cast<double>(std::chrono::duration_cast<std::chrono::microseconds>(
						rgbImage.get_device_timestamp()).count());

					std::string filename_rgb = std::to_string(time_rgb / 1000000) + ".png";

					double time_d = static_cast<double>(std::chrono::duration_cast<std::chrono::microseconds>(
						depthImage.get_device_timestamp()).count());

					std::string filename_d = std::to_string(time_d / 1000000) + ".png";

					double time_ir = static_cast<double>(std::chrono::duration_cast<std::chrono::microseconds>(
						irImage.get_device_timestamp()).count());
					std::string filename_ir = std::to_string(time_ir / 1000000) + ".png";
					imwrite("./RGB/" + filename_rgb, cv_rgbImage_8U);
					imwrite("./Depth/" + filename_d, cv_depth_8U);
					imwrite("./Ir/" + filename_ir, cv_irImage_8U);

					k4a_image_t xy_table = NULL;
					k4a_image_t point_cloud = NULL;
					int point_count = 0;

					double time_point = static_cast<double>(std::chrono::duration_cast<std::chrono::microseconds>(
						rgbImage.get_device_timestamp()).count());
					std::string filename_point = "ply/" + std::to_string(time_point / 1000000) + ".ply";

					k4a_image_create(K4A_IMAGE_FORMAT_CUSTOM,
						k4aCalibration.depth_camera_calibration.resolution_width,
						k4aCalibration.depth_camera_calibration.resolution_height,
						k4aCalibration.depth_camera_calibration.resolution_width * (int)sizeof(k4a_float2_t),
						&xy_table);

					create_xy_table(&k4aCalibration, xy_table);

					k4a_image_create(K4A_IMAGE_FORMAT_CUSTOM,
						k4aCalibration.depth_camera_calibration.resolution_width,
						k4aCalibration.depth_camera_calibration.resolution_height,
						k4aCalibration.depth_camera_calibration.resolution_width * (int)sizeof(k4a_float3_t),
						&point_cloud);

					if (depthImage == 0)
					{
						printf("Failed to get depth image from capture\n");
					}

					generate_point_cloud(depthImage, xy_table, point_cloud, &point_count);

					write_point_cloud(filename_point.c_str(), point_cloud, point_count);

					k4a_image_release(xy_table);
					k4a_image_release(point_cloud);

					std::cout << "Acquiring!" << endl;

					//写入depth.txt, rgb.txt文件
					rgb_out << std::to_string(time_rgb / 1000000) << "    " << "rgb/" << filename_rgb << endl;
					d_out << std::to_string(time_d / 1000000) << "    " << "depth/" << filename_d << endl;
					ir_out << std::to_string(time_ir / 1000000) << "    " << "ir/" << filename_ir << endl;

					imshow("Kinect color frame", rgbFrame);
				}
			}
		}
		if (waitKey(30) == 27 || waitKey(30) == 'q')
		{
			device.close();
			std::cout << "----------------------------------" << std::endl;
			std::cout << "------------- closed -------------" << std::endl;
			std::cout << "----------------------------------" << std::endl;
			break;
		}
	}
}

Kinect 机器视觉 openCV C++三维点云开发

转载原出处：https://blog.csdn.net/CH_monsy/article/details/113308254

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下一篇：深度图像转换为点云数据计算原理及代码实现

基于OpenCV和KinectSDK实现Azure Kinect DK实时获取RGB、深度图像及红外图像、交互式数据采集将图片数据保存本地

Robot_Tiger_

基于OpenCV和KinectSDK实现Azure Kinect DK实时获取RGB、深度图像及红外图像、交互式数据采集将图片数据保存本地

1.开发环境

2.采集效果图

2.1采集界面

2.2RGB图像

2.3 深度图像

2.4红外图像

3.深度图计算原理

4.代码主要函数及其功能

4.1配置并启动Kinect设备

4.2声明数据结构

4.3创建txt文件流

4.4 循环获取每一帧的图像

4.5数据格式转换

4.6通过捕获键盘进行交互式数据采集

5.完整代码

5.1头文件ShowDep_Color.h

5.2ShowDep_Color.cpp

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