ROS下使用intel Realsense摄像头进行人脸检测

转载地址：http://blog.csdn.net/may0324/article/details/50984752

使用准备条件：

ROS-indigo 

intel Realsense摄像头（我使用的依旧是R200）

确保已经正常安装驱动，安装方法见博文

http://blog.csdn.net/may0324/article/details/50981540

1.首先到github下载ros-realsense源码包,该包包含已经定义好的packages和nodes

https://github.com/intel-ros/realsense

2.新建工作区目录，如

mkdir workspace

3.进入创建的工作区目录，病创建文件目录，命名为src

cd workspace
mkdir src

4.进入src目录，并将下载包中的camera解压到此目录内

5.部署完成后就可以编译了，在workspace目录下执行命令

catkin_make

6.编译成功后，执行

source devel/setup.bash

这个自动生成的脚本文件设置了若干环境变量,从而使 ROS 能够找到你创建的功能包和新生成的可执行文件

7.完成后就可以执行包中自带的launch文件打开摄像头了

roslaunch realsense_camera realsense_r200_nodelet_standalone_preset.launch

启动后发现摄像头灯亮起，表明在工作，然而并没有什么新奇的东西发生，这是为什么呢？

其实是因为该节点只是在不断的发布消息（就是不同形式的图像信息，如RGB，红外，深度，点云等），但是并没有节点订阅该消息，所以为了观看摄像头拍摄的图像，我们需要再写一个节点订阅该消息，并将图像显示出来，接着再进行人脸检测等后续功能。为了以后功能拓展方便，我们直接写个新的功能包（package)。

这里我们定义的场景是，该功能节点订阅realsense发布的图像消息，解码并显示出来，同时调用OpenCV的haar分类器进行人脸检测，并将检测到的人脸封装成消息发布出去，所以这个节点本身是个订阅者(subscriber)，同时也是个发布者(publisher）。

1.进入workspace的src目录内，创建包目录

catkin_create_pkg client std_msgs rospy roscpp

这里面 std_msgs rospy roscpp 是我们通常一个最基本的C++包之中需要的依赖项。后面这些依赖项均可以通过配置 package.xml 进行更改

2.进入创建的包client目录中，创建msg文件夹，并创建需要发布的人脸消息文件facebox.msg和faces.msg：

facebox.msg

uint16 top
uint16 left
uint16 width
uint16 height

faces.msg

facebox[] face_boxes
uint16 image_width
uint16 image_height

3. 进入client/src目录中，创建主程序文件client.cpp，并写入如下内容

#include <ros/ros.h>
#include <image_transport/image_transport.h>
#include <cv_bridge/cv_bridge.h>
#include <sensor_msgs/image_encodings.h>
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <std_msgs/String.h>

#include <client/faces.h>
#include <client/facebox.h>

using namespace std;
using namespace cv ;

CascadeClassifier face_cascade ;
bool showResult = true ;
ros::Publisher pub;
int frame_id = 0 ;

vector<Rect> detectFaces(Mat frame) {
    vector<Rect> faces;
    Mat bufferMat;
    cvtColor(frame, bufferMat, COLOR_BGR2GRAY);
    equalizeHist(bufferMat, bufferMat);
    face_cascade.detectMultiScale(bufferMat, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));
    return faces;
}

void imageCB(const sensor_msgs::ImageConstPtr& msg) {
    cv_bridge::CvImagePtr cvPtr;
    try {
        cvPtr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);
    } catch (cv_bridge::Exception& e) {
        ROS_ERROR("cv_bridge exception: %s", e.what());
        return;
    }

    vector<Rect> faces = detectFaces(cvPtr->image);
    client::faces faces_msg;
    client::facebox _facebox;
    faces_msg.image_width = cvPtr->image.cols;
    faces_msg.image_height = cvPtr->image.rows;

    for (int i = 0; i < faces.size(); i++) {
        _facebox.top = faces[i].y;
        _facebox.left = faces[i].x;
        _facebox.width = faces[i].width;
        _facebox.height = faces[i].height;
        faces_msg.face_boxes.push_back(_facebox);
        if (showResult)
            rectangle(cvPtr->image, faces[i], CV_RGB(100, 100, 255), 1);
    }

    frame_id++ ;

    pub.publish(faces_msg);
    if (showResult) {
        imshow("Live Feed", cvPtr->image);
        waitKey(3);
    }
}


int main(int argc, char **argv) {
   face_cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");
    ros::init(argc, argv, "client");
    ros::NodeHandle nh;
    nh.param("/client/showResult", showResult, true);
    pub = nh.advertise<client::faces>("/faces", 5);
    ros::Subscriber sub = nh.subscribe("/camera/color/image_raw", 1, imageCB);
    ros::spin();
}

4.修改package.xml，添加所需依赖项

<buildtool_depend>catkin</buildtool_depend>
<build_depend>roscpp</build_depend>
<build_depend>rospy</build_depend>
<build_depend>std_msgs</build_depend>
<build_depend>cv_bridge</build_depend>
<build_depend>image_transport</build_depend>
<build_depend>message_generation</build_depend>
<build_depend>sensor_msgs</build_depend>
<build_depend>realsense_camera</build_depend>
<run_depend>cv_bridge</run_depend>
<run_depend>image_transport</run_depend>
<run_depend>roscpp</run_depend>
<run_depend>rospy</run_depend>
<run_depend>std_msgs</run_depend>
<run_depend>sensor_msgs</run_depend>
<run_depend>message_runtime</run_depend>
<run_depend>realsense_camera</run_depend>

其中的realsense_camera就是我们要依赖的realsen_camera库

5.修改CMakeLists.txt，文件内容如下

cmake_minimum_required(VERSION 2.8.3)
project(client)

## Find catkin macros and libraries
## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
## is used, also find other catkin packages
find_package(catkin REQUIRED COMPONENTS
  cv_bridge
  image_transport
  roscpp
  rospy
  std_msgs
  sensor_msgs
  message_generation
  realsense_camera
)
find_package(OpenCV REQUIRED)
## System dependencies are found with CMake's conventions
# find_package(Boost REQUIRED COMPONENTS system)


## Uncomment this if the package has a setup.py. This macro ensures
## modules and global scripts declared therein get installed
## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
# catkin_python_setup()

################################################
## Declare ROS messages, services and actions ##
################################################

## To declare and build messages, services or actions from within this
## package, follow these steps:
## * Let MSG_DEP_SET be the set of packages whose message types you use in
##   your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
## * In the file package.xml:
##   * add a build_depend tag for "message_generation"
##   * add a build_depend and a run_depend tag for each package in MSG_DEP_SET
##   * If MSG_DEP_SET isn't empty the following dependency has been pulled in
##     but can be declared for certainty nonetheless:
##     * add a run_depend tag for "message_runtime"
## * In this file (CMakeLists.txt):
##   * add "message_generation" and every package in MSG_DEP_SET to
##     find_package(catkin REQUIRED COMPONENTS ...)
##   * add "message_runtime" and every package in MSG_DEP_SET to
##     catkin_package(CATKIN_DEPENDS ...)
##   * uncomment the add_*_files sections below as needed
##     and list every .msg/.srv/.action file to be processed
##   * uncomment the generate_messages entry below
##   * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)

## Generate messages in the 'msg' folder
 add_message_files(
   FILES
#   Message1.msg
   facebox.msg
   faces.msg
 )

## Generate services in the 'srv' folder
# add_service_files(
#   FILES
#   Service1.srv
#   Service2.srv
# )

## Generate actions in the 'action' folder
# add_action_files(
#   FILES
#   Action1.action
#   Action2.action
# )

## Generate added messages and services with any dependencies listed here
 generate_messages(
   DEPENDENCIES
   std_msgs
   geometry_msgs
 )

################################################
## Declare ROS dynamic reconfigure parameters ##
################################################

## To declare and build dynamic reconfigure parameters within this
## package, follow these steps:
## * In the file package.xml:
##   * add a build_depend and a run_depend tag for "dynamic_reconfigure"
## * In this file (CMakeLists.txt):
##   * add "dynamic_reconfigure" to
##     find_package(catkin REQUIRED COMPONENTS ...)
##   * uncomment the "generate_dynamic_reconfigure_options" section below
##     and list every .cfg file to be processed

## Generate dynamic reconfigure parameters in the 'cfg' folder
# generate_dynamic_reconfigure_options(
#   cfg/DynReconf1.cfg
#   cfg/DynReconf2.cfg
# )

###################################
## catkin specific configuration ##
###################################
## The catkin_package macro generates cmake config files for your package
## Declare things to be passed to dependent projects
## INCLUDE_DIRS: uncomment this if you package contains header files
## LIBRARIES: libraries you create in this project that dependent projects also need
## CATKIN_DEPENDS: catkin_packages dependent projects also need
## DEPENDS: system dependencies of this project that dependent projects also need
catkin_package(
  #INCLUDE_DIRS include
  #LIBRARIES client
  #CATKIN_DEPENDS roscpp rospy std_msgs
  #DEPENDS system_lib
  CATKIN_DEPENDS message_runtime
)

###########
## Build ##
###########

## Specify additional locations of header files
## Your package locations should be listed before other locations
# include_directories(include)
include_directories(
  ${catkin_INCLUDE_DIRS}
  ${OpenCV_INCLUDE_DIRS}
)

## Declare a C++ library
# add_library(client
#   src/${PROJECT_NAME}/client.cpp
# )

## Add cmake target dependencies of the library
## as an example, code may need to be generated before libraries
## either from message generation or dynamic reconfigure
# add_dependencies(client ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})

## Declare a C++ executable
# add_executable(client_node src/client_node.cpp)

## Add cmake target dependencies of the executable
## same as for the library above
# add_dependencies(client_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})

## Specify libraries to link a library or executable target against
# target_link_libraries(client_node
#   ${catkin_LIBRARIES}
# )

#############
## Install ##
#############

# all install targets should use catkin DESTINATION variables
# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html

## Mark executable scripts (Python etc.) for installation
## in contrast to setup.py, you can choose the destination
# install(PROGRAMS
#   scripts/my_python_script
#   DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
# )

## Mark executables and/or libraries for installation
# install(TARGETS client client_node
#   ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
#   LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
#   RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
# )

## Mark cpp header files for installation
# install(DIRECTORY include/${PROJECT_NAME}/
#   DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
#   FILES_MATCHING PATTERN "*.h"
#   PATTERN ".svn" EXCLUDE
# )

## Mark other files for installation (e.g. launch and bag files, etc.)
# install(FILES
#   # myfile1
#   # myfile2
#   DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
# )

#############
## Testing ##
#############

## Add gtest based cpp test target and link libraries
# catkin_add_gtest(${PROJECT_NAME}-test test/test_client.cpp)
# if(TARGET ${PROJECT_NAME}-test)
#   target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
# endif()

## Add folders to be run by python nosetests
# catkin_add_nosetests(test)

add_executable(
    client
    src/client.cpp
)
target_link_libraries(
    client
    ${catkin_LIBRARIES}
    ${OpenCV_LIBS}
)

6.最后最重要的是修改realsense的camera目录下的CMakeLists.txt文件，因为原文件并没有生成别的功能包所能使用的库，所以如果不修改，由于功能包间是独立的，client功能包将找不到realsense_camera功能包，从而编译错误。红字为添加的部分

find_package(PkgConfig REQUIRED)

add_service_files(
  FILES
  cameraConfiguration.srv
)

#add dynamic reconfigure api
generate_dynamic_reconfigure_options(
  cfg/camera_params.cfg
)

#此段很重要，表明要生成别的功能包所能使用的库
catkin_package(
#  INCLUDE_DIRS include
  LIBRARIES ${PROJECT_NAME}
)

include_directories(
  ${catkin_INCLUDE_DIRS}
)

add_library(realsense_camera src/realsense_camera_nodelet.cpp)
target_link_libraries(realsense_camera
  ${catkin_LIBRARIES}
  /usr/local/lib/librealsense.so
)
add_dependencies(realsense_camera realsense_camera_generate_messages_cpp ${PROJECT_NAME}_gencfg)

7.全部修改完成后，编译。在client目录中新建launch目录，并在该目录中新建一个.launch文件，名字随便起，内容则是同时启动realsense_camera的节点和client的节点，内容如下：

<launch>

    <arg name="mode" default="preset" />
    <arg name="enable_depth" default="true" />
    <arg name="enable_color" default="true" />
    <arg name="enable_pointcloud" default="true" />
    <arg name="enable_tf" default="true" />

    <node pkg="nodelet" type="nodelet" name="standalone_nodelet"  args="manager" output="screen"/>

    <node pkg="nodelet" type="nodelet" name="RealsenseNodelet"
        args="load realsense_camera/RealsenseNodelet standalone_nodelet">
        <param name="mode"              type="str"  value="$(arg mode)" />
        <param name="enable_depth"      type="bool" value="$(arg enable_depth)" />
        <param name="enable_color"      type="bool" value="$(arg enable_color)" />
        <param name="enable_pointcloud" type="bool" value="$(arg enable_pointcloud)" />
        <param name="enable_tf"         type="bool" value="$(arg enable_tf)" />
    </node>

    <node pkg="client" type="client" name="clientnode"  output="screen">
          <param name="showResult" value="true"/>
        </node>
</launch>

8.最后别忘了source devel/setup.bash。然后就可以启动啦

roslaunch client detectface.launch