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因为D430是可以使用SDK2.0 的，我终于可以研究这个SDK了，好不容易啊。 支持D430，嘤嘤嘤。然后Intel觉得这个产品线不值钱，就不开发了，真是遗憾。 由此开始我们的SDK2.0学习之旅。 提供了一些方便的开发工具 可以快速查看，以及评估图像质量，对设备本身的固件管理等。 除了EXE文件，还有一些源码 界面是使用IMGUI构建的： 使用IMGUI构建的复杂GUI 还有GLFW： 支持Python接口 D400系列的相机可以开启一些高级的功能，一旦开启就自动的保存在闪存中，开机就自动启动 GitHub上面的Wiki是我们感兴趣的地方，甚至文档还很好。
可以及时的去发issue，回复的很及时。 rs::context ctx;if (ctx.get_device_count() == 0) throw std::runtime_error("No device detected. Is it plugged in?");rs::device & dev = *ctx.get_device(0);查询相机是不是正常连接，这是SDK1.0 的写法。 rs2::context ctx;auto list = ctx.query_devices(); // Get a snapshot of currently connected devicesif (list.size() == 0) throw std::runtime_error("No device detected. Is it plugged in?");rs2::device dev = list.front();这是2.0的写法 rs2::pipeline pipe;pipe.start();开始数据传输 rs2::config cfg;cfg.enable_stream(RS2_STREAM_INFRARED, 0);cfg.enable_stream(RS2_STREAM_INFRARED, 1);rs2::pipeline pipe;pipe.start(cfg);启动左右的深度摄像头 rs2::pipeline pipe;pipe.start();rs2::frameset frames = pipe.wait_for_frames();rs2::frame frame = frames.first(RS2_STREAM_DEPTH);if (frame) frame.get_data(); // Pointer to depth pixels, // invalidated when last copy of frame goes out of scope启动帧同步 rs2::pipeline pipe;pipe.start();rs2::frameset frames;if (pipe.poll_for_frames(&frames)){ rs2::frame depth_frame = frames.first(RS2_STREAM_DEPTH); depth_frame.get_data();}使用轮询的方式获得帧 rs2::pipeline pipe;pipe.start();const auto CAPACITY = 5; // allow max latency of 5 framesrs2::frame_queue queue(CAPACITY);std::thread t([&]() { while (true) { rs2::depth_frame frame; if (queue.poll_for_frame(&frame)) { frame.get_data(); // Do processing on the frame } }});t.detach();while (true){ auto frames = pipe.wait_for_frames(); queue.enqueue(frames.get_depth_frame());}使用线程获得帧 rs2::pipeline pipe;rs2::pipeline_profile selection = pipe.start();auto depth_stream = selection.get_stream(RS2_STREAM_DEPTH);auto color_stream = selection.get_stream(RS2_STREAM_COLOR);rs2_extrinsics e = depth_stream.get_extrinsics_to(color_stream);// Apply extrinsics to the originfloat origin[3] { 0.f, 0.f, 0.f };float target[3];rs2_transform_point_to_point(target, &e, origin);获得内参 rs2::pipeline pipe;rs2::pipeline_profile selection = pipe.start();auto depth_stream = selection.get_stream(RS2_STREAM_DEPTH) .as<rs2::video_stream_profile>();auto resolution = std::make_pair(depth_stream.width(), depth_stream.height());auto i = depth_stream.get_intrinsics();auto principal_point = std::make_pair(i.ppx, i.ppy);auto focal_length = std::make_pair(i.fx, i.fy);rs2_distortion model = i.model;获得视频流的内参 rs2::pipeline pipe;rs2::pipeline_profile selection = pipe.start();auto depth_stream = selection.get_stream(RS2_STREAM_DEPTH) .as<rs2::video_stream_profile>();auto i = depth_stream.get_intrinsics();float fov[2]; // X, Y fovrs2_fov(&i, fov);获得FOV参数 rs2::pipeline pipe;rs2::pipeline_profile selection = pipe.start();// Find first depth sensor (devices can have zero or more then one)auto sensor = selection.get_device().first<rs2::depth_sensor>();auto scale = sensor.get_depth_scale();获得深度的单位 rs2::pipeline pipe; rs2::pipeline_profile selection = pipe.start();rs2::device selected_device = selection.get_device();auto depth_sensor = selected_device.first<rs2::depth_sensor>();if (depth_sensor.supports(RS2_OPTION_EMITTER_ENABLED)){ depth_sensor.set_option(RS2_OPTION_EMITTER_ENABLED, 1.f); // Enable emitter depth_sensor.set_option(RS2_OPTION_EMITTER_ENABLED, 0.f); // Disable emitter}if (depth_sensor.supports(RS2_OPTION_LASER_POWER)){ // Query min and max values: auto range = depth_sensor.get_option_range(RS2_OPTION_LASER_POWER); depth_sensor.set_option(RS2_OPTION_LASER_POWER, range.max); // Set max power depth_sensor.set_option(RS2_OPTION_LASER_POWER, 0.f); // Disable laser}SDK可以自由的按需要编译，使用Cmake控制编译过程。 # First import the libraryimport pyrealsense2 as rs# Create a context object. This object owns the handles to all connected realsense devicespipeline = rs.pipeline()pipeline.start()try: while True: # Create a pipeline object. This object configures the streaming camera and owns it's handle frames = pipeline.wait_for_frames() depth = frames.get_depth_frame() if not depth: continue # Print a simple text-based representation of the image, by breaking it into 10x20 pixel regions and approximating the coverage of pixels within one meter coverage = [0]*64 for y in xrange(480): for x in xrange(640): dist = depth.get_distance(x, y) if 0 < dist and dist < 1: coverage[x/10] += 1 if y%20 is 19: line = "" for c in coverage: line += " .:nhBXWW"[c/25] coverage = [0]*64 print(line)finally: pipeline.stop()Python接口使用起来很简单 import numpy as npdepth = frames.get_depth_frame()depth_data = depth.as_frame().get_data()np_image = np.asanyarray(depth_data)SDK还支持缓冲协议，所以使用Numpy的性能会更好。 function depth_example() % Make Pipeline object to manage streaming pipe = realsense.pipeline(); % Make Colorizer object to prettify depth output colorizer = realsense.colorizer(); % Start streaming on an arbitrary camera with default settings profile = pipe.start(); % Get streaming device's name dev = profile.get_device(); name = dev.get_info(realsense.camera_info.name); % Get frames. We discard the first couple to allow % the camera time to settle for i = 1:5 fs = pipe.wait_for_frames(); end % Stop streaming pipe.stop(); % Select depth frame depth = fs.get_depth_frame(); % Colorize depth frame color = colorizer.colorize(depth); % Get actual data and convert into a format imshow can use % (Color data arrives as [R, G, B, R, G, B, ...] vector) data = color.get_data(); img = permute(reshape(data',[3,color.get_width(),color.get_height()]),[3 2 1]); % Display image imshow(img); title(sprintf("Colorized depth frame from %s", name));end对于一个搞数学建模的人，对MATLAB有莫名的好感。这里官方也简单的左了MATLAB的包装。 Openni2也是支持的，不过需要自己把底层的驱动做修改。
下载Openni2
一个八九年前的库居然生命力这么顽强。
CMake里面需要添加这些源码进行编译
这些就是源码，等我CPP学的屌屌的，我就写源码分析。
构建SDK的推荐配置 视觉应用难免会遇到提取前景这种需求，GrabCut这个算法一般是经常使用的
这个算法的来源是OpenCV
这里SDK提供了深度的前景提取算法 z https://github.com/IntelRealSense/librealsensehttps://github.com/ocornut/imguihttps://www.glfw.org/community.html#bindingshttps://github.com/IntelRealSense/librealsense/wiki/Troubleshooting-Q&Ahttps://structure.io/opennihttps://docs.opencv.org/3.4/d8/d83/tutorial_py_grabcut.html |
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