RGB-D相机（Azure Kinect DK）RGB图、深度图的获取，配准与保存

文章目录

RGB-D相机（Azure Kinect DK）RGB图、深度图的获取，配准与保存1.C++代码：2. CMakeLists3. 编译运行：4. 采集示例5. 参考

        前提：官方apt安装Azure Kinect 传感器 SDK

        代码AcquiringImages将原始深度图转换到RGB摄像头坐标系下，得到的配准后的深度图，并将转换后的depth图，原始RGB图、原始IR图保存在本地，采集格式仿照TUM数据集，图片的命名格式为时间戳+.png后缀。

1.C++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" << "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; #endif // ir // * Each pixel of IR16 data is two bytes of little endian unsigned depth data. The value of the data represents // * brightness. irImage = capture.get_ir_image(); #if DEBUG_std_cout == 1 std::cout << "[ir] " << "\n" << "format: " << irImage.get_format() << "\n" << "device_timestamp: " << irImage.get_device_timestamp().count() << "\n" << "system_timestamp: " << irImage.get_system_timestamp().count() << "\n" << "height*width: " << irImage.get_height_pixels() << ", " << irImage.get_width_pixels() << std::endl; #endif //深度图和RGB图配准 k4a::calibration k4aCalibration = device.get_calibration(config.depth_mode, config.color_resolution);// Get the camera calibration for the entire K4A device, which is used for all transformation functions. 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())); cv_depth.convertTo(cv_depth_8U, 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())); cv_irImage.convertTo(cv_irImage_8U, CV_8U, 1); // show image cv::imshow("color", cv_rgbImage_no_alpha); cv::imshow("depth", cv_depth_8U); cv::imshow("ir", cv_irImage_8U); // 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_no_alpha); imwrite("./depth/"+filename_d,cv_depth_8U); imwrite("./ir/"+filename_ir, cv_irImage_8U); 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; rgb_out<<flush; d_out<<flush; ir_out<<flush; cv_rgbImage_with_alpha.release(); cv_rgbImage_no_alpha.release(); cv_depth.release(); cv_depth_8U.release(); cv_irImage.release(); cv_irImage_8U.release(); capture.reset(); //if (cv::waitKey(0) == 'q') {//按键采集，用户按下'q',跳出循环,结束采集 // std::cout << "----------------------------------" << std::endl; // std::cout << "------------- closed -------------" << std::endl; // std::cout << "----------------------------------" << std::endl; // break; // } } else { std::cout << "false: K4A_WAIT_RESULT_TIMEOUT." << std::endl; } } cv::destroyAllWindows(); rgb_out<<flush; d_out<<flush; ir_out<<flush; rgb_out.close(); d_out.close(); ir_out.close(); // 释放，关闭设备 rgbImage.reset(); depthImage.reset(); irImage.reset(); capture.reset(); device.close(); return 1; }

2. CMakeLists

# Enable C++11 set(CMAKE_CXX_FLAGS "-std=c++11") set(CMAKE_CXX_STANDARD_REQUIRED TRUE) # Define project name project(AcquiringImages) # Kinect DK相机 find_package(k4a REQUIRED)# 后面的target_link_libraries中用到了k4a::k4a # Find OpenCV find_package(OpenCV REQUIRED) # Add includes include_directories( ${OpenCV_INCLUDE_DIRS} ) # Declare the executable target built from your sources add_executable(AcquiringImages main.cpp) # Link your application with other libraries target_link_libraries(AcquiringImages k4a::k4a ${OpenCV_LIBS})

3. 编译运行：

进入源码目录：

mkdir build cd build mkdir rgb depth ir//未创建rgb depth ir文件夹 cmake .. -GNinja ninja ./AcquiringImages

闭终端或终止程序即可结束图像的采集。

结束图像采集后，将RGB图和深度图相关联：

进入源码目录：

cd build python associate.py rgb.txt depth.txt > ./associations.txt

4. 采集示例

以下为409实验室采集的图片示例：

RGB图:

深度图：

IR图：

5. 参考

https://cloud.tencent.com/developer/column/81192