intel的realsense2安装对齐

安装步骤：

参考：

https://blog.csdn.net/z17816876284/article/details/79159518

https://blog.csdn.net/qq_28467367/article/details/93381035

https://github.com/IntelRealSense/librealsense/blob/master/doc/distribution_linux.md

https://blog.csdn.net/Sparta_117/article/details/77851876  在ROS Kinetic环境下使用RealSense

关于pyrealsense2 2.30.0.1177

看这个

https://www.zhihu.com/question/298545345/answer/515573083

测试系统Ubuntu16.04

运行代码

uname -r

如果>=4.4.0-50的版本则ok，否则需要升级内核。

内核更新步骤：

https://blog.csdn.net/z17816876284/article/details/79159518

依赖：

sudo apt-get install git libssl-dev libusb-1.0-0-dev pkg-config libglfw3-dev
sudo apt-get install libgtk-3-dev
sudo apt-get install libglew-dev

 

apt-get安装方式：

第1步：

sudo apt-key adv --keyserver keys.gnupg.net --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE

第2步：

sudo add-apt-repository "deb http://realsense-hw-public.s3.amazonaws.com/Debian/apt-repo xenial main" -u

第3步：很慢，不知道为啥

sudo apt-get install librealsense2-dkms
sudo apt-get install librealsense2-utils

测试：

打开终端输入：realsense-viewer

此时并没有安装开发包，无法编程

第4步：这一步我没尝试，太慢了。。。。。

sudo apt-get install librealsense2-dev
sudo apt-get install librealsense2-dbg

第4步我采样自己编译的方式：

https://github.com/IntelRealSense/librealsense/blob/master/doc/installation.md

下载：

https://github.com/IntelRealSense/librealsense

sudo cp config/99-realsense-libusb.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules && udevadm trigger
./scripts/setup_udev_rules.sh
./scripts/patch-realsense-ubuntu-lts.sh

查看是否成功：

lsmod | grep videobuf2_core

sudo dmesg | tail -n 50

编译：

mkdir build && cd build
cmake ../ -DBUILD_EXAMPLES=true
sudo make uninstall && make clean && make -j8 && sudo make install

一直卡在了：

Building with TM2

https://github.com/IntelRealSense/librealsense/issues/3125

https://github.com/IntelRealSense/librealsense/issues/3182

https://github.com/IntelRealSense/librealsense/issues/2737

https://github.com/IntelRealSense/librealsense/issues/2719#issuecomment-474823042

打开cmakelist.txt

显示

Internet connection identified
Info: REALSENSE_VERSION_STRING=2.29.0
Setting Unix configurations
using RS2_USE_V4L2_BACKEND
GLFW 3.3 not found; using internal version
Could NOT find Vulkan (missing:  VULKAN_LIBRARY VULKAN_INCLUDE_DIR) 
Using X11 for window creation
Could NOT find apriltag (missing:  APRILTAG_INC APRILTAG_LIB) 
Unable to find apriltag library, skipping pose-apriltag example
Fetching recommended firmwares:
D4XX_RC_VERSION: 5.11.15.0
D4XX_FW_VERSION: 5.11.15.0
SR3XX_FW_VERSION: 3.26.1.0
Download firmware 0;"returning early; file already exists with expected SHA1 hash" for D4XX_FW_Image-5.11.15.0.bin
Download firmware 0;"returning early; file already exists with expected SHA1 hash" for D4XX_RC_Image-5.11.15.0.bin
Download firmware 0;"returning early; file already exists with expected SHA1 hash" for SR3XX_FW_Image-3.26.1.0.bin
Configuring done
Generating done

这里会有一点慢 

Download firmware 0;"returning early; file already exists with expected SHA1 hash" for D4XX_FW_Image-5.11.15.0.bin
Download firmware 0;"returning early; file already exists with expected SHA1 hash" for D4XX_RC_Image-5.11.15.0.bin
Download firmware 0;"returning early; file already exists with expected SHA1 hash" for SR3XX_FW_Image-3.26.1.0.bin

勾选Python选项

 

换成其他Python，带有torch环境的，后续检测+深度

https://blog.csdn.net/qq_22598167/article/details/89948089

https://www.cnblogs.com/z1141000271/p/10554341.html

 

勾选example

 

mkdir build && cd build
cmake ../ -DBUILD_EXAMPLES=true
sudo make uninstall && make clean && make -j8 && sudo make install

测试一下，打开install

./realsense-viewer

进行深度图对齐：

CMakeLists.txt

cmake_minimum_required(VERSION 3.1.0)

#demo_realsense
project(realsense_example)

set(CMAKE_CXX_STANDARD 11)

set(DEPENDENCIES realsense2)
find_package(OpenCV REQUIRED)
INCLUDE_DIRECTORIES(
        /usr/local/include/librealsense2/
)
INCLUDE_DIRECTORIES(
        ${OpenCV_INCLUDE_DIRS}
)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(realsense_example realsense_example.cpp)
target_link_libraries(realsense_example ${DEPENDENCIES})
target_link_libraries(realsense_example ${OpenCV_LIBS})

realsense_example.cpp

#include 
using namespace std;
#include 
#include 
#include 
#include 
#include 

#include
#include
#include
using namespace cv;

#include

//获取深度像素对应长度单位转换
float get_depth_scale(rs2::device dev);

//检查摄像头数据管道设置是否改变
bool profile_changed(const std::vector& current, const std::vector& prev);

int main(int argc, char * argv[]) try
{
    // Create and initialize GUI related objects
    //创建gui窗口
    //window app(1280, 720, "CPP - Align Example"); // Simple window handling
    //ImGui_ImplGlfw_Init(app, false);      // ImGui library intializition
    const char* depth_win="depth_Image";
    namedWindow(depth_win,WINDOW_AUTOSIZE);
    const char* color_win="color_Image";
    namedWindow(color_win,WINDOW_AUTOSIZE);

    //深度图像颜色map
    rs2::colorizer c;                          // Helper to colorize depth images
    //helper用于渲染图片
    //texture renderer;                     // Helper for renderig images

    // Create a pipeline to easily configure and start the camera
    //创建数据管道
    rs2::pipeline pipe;
    rs2::config pipe_config;
    pipe_config.enable_stream(RS2_STREAM_DEPTH,640,480,RS2_FORMAT_Z16,30);
    pipe_config.enable_stream(RS2_STREAM_COLOR,640,480,RS2_FORMAT_BGR8,30);
    //Calling pipeline's start() without any additional parameters will start the first device
    //直接start()，不添加配置参数，则默认打开第一个设备
    // with its default streams.
    //以及以默认的配置进行流输出
    //The start function returns the pipeline profile which the pipeline used to start the device
    //start()函数返回数据管道的profile
    rs2::pipeline_profile profile = pipe.start(pipe_config);

    // Each depth camera might have different units for depth pixels, so we get it here
    //每个深度摄像头有不同单元的像素，我们这里获取
    // Using the pipeline's profile, we can retrieve the device that the pipeline uses
    //使用数据管道的profile获取深度图像像素对应于长度单位（米）的转换比例
    float depth_scale = get_depth_scale(profile.get_device());

    //Pipeline could choose a device that does not have a color stream
    //数据管道可以选择一个没有彩色图像数据流的设备
    //If there is no color stream, choose to align depth to another stream
    //选择彩色图像数据流来作为对齐对象
    rs2_stream align_to = RS2_STREAM_COLOR;//find_stream_to_align(profile.get_stream());

    /*
     @这里的对齐是改变深度图，而不改变color图
    */
    // Create a rs2::align object.
    //创建一个rs2::align的对象
    // rs2::align allows us to perform alignment of depth frames to others frames
    //rs2::align 允许我们去实现深度图像对齐其他图像
    //The "align_to" is the stream type to which we plan to align depth frames.
    // "align_to"是我们打算用深度图像对齐的图像流
    rs2::align align(align_to);

    // Define a variable for controlling the distance to clip
    //定义一个变量去转换深度到距离
    float depth_clipping_distance = 1.f;

    while (cvGetWindowHandle(depth_win)&&cvGetWindowHandle(color_win)) // Application still alive?
    {
        // Using the align object, we block the application until a frameset is available
        //堵塞程序直到新的一帧捕获
        rs2::frameset frameset = pipe.wait_for_frames();

        // rs2::pipeline::wait_for_frames() can replace the device it uses in case of device error or disconnection.
        // Since rs2::align is aligning depth to some other stream, we need to make sure that the stream was not changed
        //因为rs2::align 正在对齐深度图像到其他图像流，我们要确保对齐的图像流不发生改变
        //  after the call to wait_for_frames();
        if (profile_changed(pipe.get_active_profile().get_streams(), profile.get_streams()))
        {
            //If the profile was changed, update the align object, and also get the new device's depth scale
            //如果profile发生改变，则更新align对象，重新获取深度图像像素到长度单位的转换比例
            profile = pipe.get_active_profile();
            align = rs2::align(align_to);
            depth_scale = get_depth_scale(profile.get_device());
        }

        //Get processed aligned frame
        //获取对齐后的帧
        auto processed = align.process(frameset);

        // Trying to get both other and aligned depth frames
        //尝试获取对齐后的深度图像帧和其他帧
        rs2::frame aligned_color_frame = processed.get_color_frame();//processed.first(align_to);
        rs2::frame aligned_depth_frame = processed.get_depth_frame().apply_filter(c);;

        //获取对齐之前的color图像
        rs2::frame before_depth_frame=frameset.get_depth_frame().apply_filter(c);
        //获取宽高
        const int depth_w=aligned_depth_frame.as().get_width();
        const int depth_h=aligned_depth_frame.as().get_height();
        const int color_w=aligned_color_frame.as().get_width();
        const int color_h=aligned_color_frame.as().get_height();
        const int b_color_w=before_depth_frame.as().get_width();
        const int b_color_h=before_depth_frame.as().get_height();
        //If one of them is unavailable, continue iteration
        if (!aligned_depth_frame || !aligned_color_frame)
        {
            continue;
        }
        //创建OPENCV类型 并传入数据
        Mat aligned_depth_image(Size(depth_w,depth_h),CV_8UC3,(void*)aligned_depth_frame.get_data(),Mat::AUTO_STEP);
        Mat aligned_color_image(Size(color_w,color_h),CV_8UC3,(void*)aligned_color_frame.get_data(),Mat::AUTO_STEP);
        Mat before_color_image(Size(b_color_w,b_color_h),CV_8UC3,(void*)before_depth_frame.get_data(),Mat::AUTO_STEP);
        //显示
        imshow(depth_win,aligned_depth_image);
        imshow(color_win,aligned_color_image);
        imshow("before aligned",before_color_image);
        waitKey(10);
    }
    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;
}

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())
        {
            return dpt.get_depth_scale();
        }
    }
    throw std::runtime_error("Device does not have a depth sensor");
}

bool profile_changed(const std::vector& current, const std::vector& prev)
{
    for (auto&& sp : prev)
    {
        //If previous profile is in current (maybe just added another)
        auto itr = std::find_if(std::begin(current), std::end(current), [&sp](const rs2::stream_profile& current_sp) { return sp.unique_id() == current_sp.unique_id(); });
        if (itr == std::end(current)) //If it previous stream wasn't found in current
        {
            return true;
        }
    }
    return false;
}

安装完python版本后可以使用python的例子：

官方：

https://github.com/IntelRealSense/librealsense/tree/master/wrappers/python/examples

python -mpip install pyglet==1.3.0

python -mpip install pyrealsense2

python -mpip install pyntcloud

# -*-coding:utf-8-*-
# 首先导入库
from ctypes import *
import os
import pyrealsense2 as rs
# 导入Numpy以便于数组操作
import numpy as np
# 导入OpenCV以便于图像渲染
import cv2

# 创建一个管道
pipeline = rs.pipeline()

# Create a config并配置要流​​式传输的管道
# 颜色和深度流的不同分辨率
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 360, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

# 开始流式传输
profile = pipeline.start(config)

# 获取深度传感器的深度标尺（参见rs-align示例进行说明）
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
print("Depth Scale is: ", depth_scale)

# 我们将删除对象的背景
#  clipping_distance_in_meters meters away
clipping_distance_in_meters = 1  # 1 meter
clipping_distance = clipping_distance_in_meters / depth_scale

# 创建对齐对象
# rs.align允许我们执行深度帧与其他帧的对齐
# “align_to”是我们计划对齐深度帧的流类型。
align_to = rs.stream.color
align = rs.align(align_to)

# Streaming循环
try:
    while True:
        # 获取颜色和深度的框架集
        frames = pipeline.wait_for_frames()
        # frames.get_depth_frame（）是640x360深度图像

        # 将深度框与颜色框对齐
        aligned_frames = align.process(frames)

        # 获取对齐的帧
        aligned_depth_frame = aligned_frames.get_depth_frame()
        # aligned_depth_frame是640x480深度图像
        color_frame = aligned_frames.get_color_frame()

        # 验证两个帧是否有效
        if not aligned_depth_frame or not color_frame:
            continue

        depth_image = np.asanyarray(aligned_depth_frame.get_data())
        color_image = np.asanyarray(color_frame.get_data())

        # # remove background - 将clips_distance以外的像素设置为灰色
        grey_color = 153
        depth_image_3d = np.dstack(
            (depth_image, depth_image, depth_image))  # depth image is 1 channel, color is 3 channels
        bg_removed = np.where((depth_image_3d > clipping_distance) | (depth_image_3d <= 0), grey_color, color_image)

        # 渲染图像
        depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
        images = np.hstack((bg_removed, depth_colormap))
        cv2.namedWindow('Align Example', cv2.WINDOW_AUTOSIZE)
        cv2.imshow('Align Example', images)
        key = cv2.waitKey(1)
        # 按esc或'q'关闭图像窗口
        if key & 0xFF == ord('q') or key == 27:
            cv2.destroyAllWindows()
            break
finally:
    pipeline.stop()