硬件

相机的原理我了解的也不甚多，看到一篇讲的很好的文章，就在这里引用了~

SR300设备的红外线发射器（IR Laser Projector）发射的“结构光”，经物体反射后会被红外线传感器（IR Camera Lens）接收。由于红外线到反射物体表面的距离不同，红外传感器捕到的“结构光”图案的位置和形状会发生变化，根据这些由实感图像处理芯片就能计算出物体表面的空间信息，再用三角测距原理进行“深度”计算，进而重现3D场景。
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版权声明：本文为CSDN博主「codekiller_」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/codekiller_/article/details/63685891

获取参数c代码

秉持着对c语言深深的热爱，我还是用c写了获取内参等硬件参数的代码。

#include <librealsense2/rs.hpp> // Include RealSense Cross Platform API
#include<iostream>
#include<opencv2/opencv.hpp>float get_depth_scale(rs2::device dev) 
{    // Go over the device's sensors    for (rs2::sensor& sensor : dev.query_sensors())    {       // Check if the sensor if a depth sensor        if (rs2::depth_sensor dpt = sensor.as<rs2::depth_sensor>())  {            return dpt.get_depth_scale();        }    }    throw std::runtime_error("Device does not have a depth sensor");
} 
int main(int argc, char * argv[]) try
{rs2::log_to_console(RS2_LOG_SEVERITY_ERROR);rs2::context ctx;auto devs = ctx.query_devices();                  ///获取设备列表int device_num = devs.size();std::cout << "device num: " << device_num << std::endl;///设备数量rs2::device dev = devs[0];char serial_number[100] = { 0 };strcpy(serial_number, dev.get_info(RS2_CAMERA_INFO_SERIAL_NUMBER));printf("serial_number: %s\n", serial_number);rs2::config cfg;cfg.enable_stream(RS2_STREAM_COLOR, 640, 480, RS2_FORMAT_BGR8, 30);///配置深度图像流：分辨率640*480，图像格式：Z16， 帧率：30帧/秒cfg.enable_stream(RS2_STREAM_DEPTH, 640, 480, RS2_FORMAT_Z16, 30);///生成Realsense管道，用来封装实际的相机设备rs2::pipeline pipe;pipe.start(cfg); ///根据给定的配置启动相机管道//获取深度比例系数rs2::pipeline_profile profile = pipe.start(cfg);;float depth_scale = get_depth_scale(profile.get_device());printf("deep_scale:%f\n", depth_scale);system("pause");rs2::frameset data;data = pipe.wait_for_frames();///等待一帧数据，默认等待5srs2::depth_frame depth = data.get_depth_frame(); ///获取深度图像数据rs2::video_frame color = data.get_color_frame();  ///获取彩色图像数据rs2::stream_profile dprofile = depth.get_profile();rs2::stream_profile cprofile = color.get_profile();///获取彩色相机内参rs2::video_stream_profile cvsprofile(cprofile);rs2_intrinsics color_intrin = cvsprofile.get_intrinsics();std::cout << "\ncolor intrinsics: ";std::cout << color_intrin.width << "  " << color_intrin.height << "  ";std::cout << color_intrin.ppx << "  " << color_intrin.ppy << "  ";std::cout << color_intrin.fx << "  " << color_intrin.fy << std::endl;std::cout << "coeffs: ";for (auto value : color_intrin.coeffs)std::cout << value << "  ";std::cout << std::endl;std::cout << "distortion model: " << color_intrin.model << std::endl;///畸变模型///获取深度相机内参rs2::video_stream_profile dvsprofile(dprofile);rs2_intrinsics depth_intrin = dvsprofile.get_intrinsics();std::cout << "\ndepth intrinsics: ";std::cout << depth_intrin.width << "  " << depth_intrin.height << "  ";std::cout << depth_intrin.ppx << "  " << depth_intrin.ppy << "  ";std::cout << depth_intrin.fx << "  " << depth_intrin.fy << std::endl;std::cout << "coeffs: ";for (auto value : depth_intrin.coeffs)std::cout << value << "  ";std::cout << std::endl;std::cout << "distortion model: " << depth_intrin.model << std::endl;///畸变模型///获取深度相机相对于彩色相机的外参，即变换矩阵: P_color = R * P_depth + Trs2_extrinsics extrin = dprofile.get_extrinsics_to(cprofile);std::cout << "\nextrinsics of depth camera to color camera: \nrotaion: " << std::endl;for (int i = 0; i < 3; ++i){for (int j = 0; j < 3; ++j) {float value = extrin.rotation[3 * i + j];std::cout << value << "  ";}std::cout << std::endl;}std::cout << std::endl;std::cout << "translation: ";for (auto value : extrin.translation)std::cout << value << "  ";std::cout << std::endl;while (1){///等待一帧数据，默认等待5sdata = pipe.wait_for_frames();rs2::depth_frame depth = data.get_depth_frame(); ///获取深度图像数据rs2::video_frame color = data.get_color_frame();  ///获取彩色图像数据int color_width = color.get_width();int color_height = color.get_height();int depth_width = depth.get_width();int depth_height = depth.get_height();if (!color || !depth) break;                ///如果获取不到数据则退出///将彩色图像和深度图像转换为Opencv格式cv::Mat image(cv::Size(color_width, color_height), CV_8UC3, (void*)color.get_data(), cv::Mat::AUTO_STEP);cv::Mat depthmat(cv::Size(depth_width, depth_height), CV_16U, (void*)depth.get_data(), cv::Mat::AUTO_STEP);///显示cv::imshow("image", image);cv::imshow("depth", depthmat);cv::waitKey(1);}system("pause");return EXIT_SUCCESS;
}
catch (const rs2::error & e)
{///捕获相机设备的异常std::cerr << "RealSense error calling " << e.get_failed_function() << "(" << e.get_failed_args() << "):\n    " << e.what() << std::endl;system("pause");return EXIT_FAILURE;
}
catch (const std::exception& e)
{std::cerr << "Other error : " << e.what() << std::endl;system("pause");return EXIT_FAILURE;
}