此示例是 Intel RealSense 相机与 OpenCV 集成的 "Hello World" 基础代码。示例将打开一个 OpenCV 的 UI 窗口,并将彩色化的深度流渲染到窗口中。以下代码片段演示了如何从 rs2::frame 创建 cv::Mat:
cpp
// License: Apache 2.0. See LICENSE file in root directory.
// Copyright(c) 2017 Intel Corporation. All Rights Reserved.
#include <librealsense2/rs.hpp> // Include RealSense Cross Platform API
#include <opencv2/opencv.hpp> // Include OpenCV API
int main(int argc, char * argv[]) try
{
// Declare depth colorizer for pretty visualization of depth data
// 建着色过滤器(Colorizer Filter)
// 该过滤器根据输入的深度帧生成彩色图像。
// 与直接使用OpenCV的区别,RealSense Colorizer 硬件加速、低延迟 依赖RealSense SDK
// cv::applyColorMap() 跨平台、可定制 需手动处理深度值归一化
rs2::colorizer color_map;
// Declare RealSense pipeline, encapsulating the actual device and sensors
rs2::pipeline pipe;
// Start streaming with default recommended configuration
pipe.start();
using namespace cv;
const auto window_name = "Display Image";
namedWindow(window_name, WINDOW_AUTOSIZE);
while (waitKey(1) < 0 && getWindowProperty(window_name, WND_PROP_AUTOSIZE) >= 0)
{
rs2::frameset data = pipe.wait_for_frames(); // Wait for next set of frames from the camera
rs2::frame depth = data.get_depth_frame().apply_filter(color_map);
// Query frame size (width and height)
// 返回图像的像素高度
const int w = depth.as<rs2::video_frame>().get_width();
const int h = depth.as<rs2::video_frame>().get_height();
// Create OpenCV matrix of size (w,h) from the colorized depth data
Mat image(Size(w, h), CV_8UC3, (void*)depth.get_data(), Mat::AUTO_STEP);
// Update the window with new data
imshow(window_name, image);
}
return EXIT_SUCCESS;
}
catch (const rs2::error & e)
{
std::cerr << "RealSense error calling " << e.get_failed_function() << "(" << e.get_failed_args() << "):\n " << e.what() << std::endl;
return EXIT_FAILURE;
}
catch (const std::exception& e)
{
std::cerr << e.what() << std::endl;
return EXIT_FAILURE;
}
此外,上面手动转换image如果是彩色图像色值会有问题,可以直接使用realsense提供的转换接口:
cpp
// License: Apache 2.0. See LICENSE file in root directory.
// Copyright(c) 2017 Intel Corporation. All Rights Reserved.
#include <librealsense2/rs.hpp> // Include RealSense Cross Platform API
#include <opencv2/opencv.hpp> // Include OpenCV API
#include "../cv-helpers.hpp"
int main(int argc, char * argv[]) try
{
// Declare depth colorizer for pretty visualization of depth data
// 建着色过滤器(Colorizer Filter)
// 该过滤器根据输入的深度帧生成彩色图像。
// 与直接使用OpenCV的区别,RealSense Colorizer 硬件加速、低延迟 依赖RealSense SDK
// cv::applyColorMap() 跨平台、可定制 需手动处理深度值归一化
rs2::colorizer color_map;
// Declare RealSense pipeline, encapsulating the actual device and sensors
rs2::pipeline pipe;
// Start streaming with default recommended configuration
pipe.start();
using namespace cv;
const auto window_name = "Display Image";
namedWindow(window_name, WINDOW_AUTOSIZE);
while (waitKey(1) < 0 && getWindowProperty(window_name, WND_PROP_AUTOSIZE) >= 0)
{
rs2::frameset data = pipe.wait_for_frames(); // Wait for next set of frames from the camera
rs2::frame depth = data.get_depth_frame().apply_filter(color_map);
rs2::frame color = data.get_color_frame();
// Query frame size (width and height)
// 返回图像的像素高度
const int w = depth.as<rs2::video_frame>().get_width();
const int h = depth.as<rs2::video_frame>().get_height();
const int cw = color.as<rs2::video_frame>().get_width();
const int ch = color.as<rs2::video_frame>().get_height();
// Create OpenCV matrix of size (w,h) from the colorized depth data
//Mat image(Size(w, h), CV_8UC3, (void*)depth.get_data(), Mat::AUTO_STEP);
Mat image = frame_to_mat(depth);
// Update the window with new data
imshow(window_name, image);
}
return EXIT_SUCCESS;
}
catch (const rs2::error & e)
{
std::cerr << "RealSense error calling " << e.get_failed_function() << "(" << e.get_failed_args() << "):\n " << e.what() << std::endl;
return EXIT_FAILURE;
}
catch (const std::exception& e)
{
std::cerr << e.what() << std::endl;
return EXIT_FAILURE;
}