目录

人脸识别

1.环境准备

2.创建工作空间与功能包

3.人脸识别程序

4.launch文件

5.执行

物体追踪 

人脸识别

1.环境准备

首先准备ROS系统，基于ros的软件支持opencv,usbcam

apt install ros-kinetic-desktop-full

apt install ros-kinetic-opencv3

apt install ros-kinetic-usb-cam

2.创建工作空间与功能包

在创建功能包时导入依赖库

$ source /opt/ros/kinetic/setup.zsh
$ mkdir -p ~/catkin_ws/src
$ cd ~/catkin_ws/src
$ catkin_init_workspace
$ cd ~/catkin_ws
$ catkin_make 
$ souce ~/catkiin_ws/devel/setup.zsh
$ cd ~/catkin_ws/src
$ catkin_create_pkg test1 rospy roscpp std_msgs sensor_msgs cv_bridge image_transport 
$ cd ~/catkin_ws
$ catkin_make
$ source devel/setup.zsh

创建文件目录结构

scripts存放代码，launch存放启动文件

$cd test1
$mkdir launch scripts

3.人脸识别程序

$cd test1/scripts

$touch face_detector.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
import cv2
import numpy as np
from sensor_msgs.msg import Image, RegionOfInterest
from cv_bridge import CvBridge, CvBridgeError

class faceDetector:
    def __init__(self):
        rospy.on_shutdown(self.cleanup);

        # 创建cv_bridge
        self.bridge = CvBridge()
        self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)

        # 获取haar特征的级联表的XML文件，文件路径在launch文件中传入
        cascade_1 = rospy.get_param("~cascade_1", "")
        cascade_2 = rospy.get_param("~cascade_2", "")

        # 使用级联表初始化haar特征检测器
        self.cascade_1 = cv2.CascadeClassifier(cascade_1)
        self.cascade_2 = cv2.CascadeClassifier(cascade_2)

        # 设置级联表的参数，优化人脸识别，可以在launch文件中重新配置
        self.haar_scaleFactor  = rospy.get_param("~haar_scaleFactor", 1.2)
        self.haar_minNeighbors = rospy.get_param("~haar_minNeighbors", 2)
        self.haar_minSize      = rospy.get_param("~haar_minSize", 40)
        self.haar_maxSize      = rospy.get_param("~haar_maxSize", 60)
        self.color = (50, 255, 50)

        # 初始化订阅rgb格式图像数据的订阅者，此处图像topic的话题名可以在launch文件中重映射
        self.image_sub = rospy.Subscriber("input_rgb_image", Image, self.image_callback, queue_size=1)

    def image_callback(self, data):
        # 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式
        try:
            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")     
            frame = np.array(cv_image, dtype=np.uint8)
        except CvBridgeError, e:
            print e

        # 创建灰度图像
        grey_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # 创建平衡直方图，减少光线影响
        grey_image = cv2.equalizeHist(grey_image)

        # 尝试检测人脸
        faces_result = self.detect_face(grey_image)

        # 在opencv的窗口中框出所有人脸区域
        if len(faces_result)>0:
            for face in faces_result: 
                x, y, w, h = face
                cv2.rectangle(cv_image, (x, y), (x+w, y+h), self.color, 2)

        # 将识别后的图像转换成ROS消息并发布
        self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8"))

    def detect_face(self, input_image):
        # 首先匹配正面人脸的模型
        if self.cascade_1:
            faces = self.cascade_1.detectMultiScale(input_image, 
                    self.haar_scaleFactor, 
                    self.haar_minNeighbors, 
                    cv2.CASCADE_SCALE_IMAGE, 
                    (self.haar_minSize, self.haar_maxSize))
                                         
        # 如果正面人脸匹配失败，那么就尝试匹配侧面人脸的模型
        if len(faces) == 0 and self.cascade_2:
            faces = self.cascade_2.detectMultiScale(input_image, 
                    self.haar_scaleFactor, 
                    self.haar_minNeighbors, 
                    cv2.CASCADE_SCALE_IMAGE, 
                    (self.haar_minSize, self.haar_maxSize))
        
        return faces

    def cleanup(self):
        print "Shutting down vision node."
        cv2.destroyAllWindows()

if __name__ == '__main__':
    try:
        # 初始化ros节点
        rospy.init_node("face_detector")
        faceDetector()
        rospy.loginfo("Face detector is started..")
        rospy.loginfo("Please subscribe the ROS image.")
        rospy.spin()
    except KeyboardInterrupt:
        print "Shutting down face detector node."
        cv2.destroyAllWindows()

4.launch文件

$cd test1/launch

$touch usb_cam.launch face_detector.launch

usb_cam.launch开启摄像头

<launch>

  <node name="usb_cam" pkg="usb_cam" type="usb_cam_node" output="screen" >
    <param name="video_device" value="/dev/video0" />
    <param name="image_width" value="640" />
    <param name="image_height" value="480" />
    <param name="pixel_format" value="yuyv" />
    <param name="camera_frame_id" value="usb_cam" />
    <param name="io_method" value="mmap"/>
  </node>

</launch>

face_detector.launch运行人脸识别程序 

<launch>
    <node pkg="test1" name="face_detector" type="face_detector.py" output="screen">
        <remap from="input_rgb_image" to="/usb_cam/image_raw" />
        <rosparam>
            haar_scaleFactor: 1.2
            haar_minNeighbors: 2
            haar_minSize: 40
            haar_maxSize: 60
        </rosparam>
        <param name="cascade_1" value="$(find robot_vision)/data/haar_detectors/haarcascade_frontalface_alt.xml" />
        <param name="cascade_2" value="$(find robot_vision)/data/haar_detectors/haarcascade_profileface.xml" />
    </node>
</launch>

5.执行

分别开启三个终端，执行以下命令

$roslaunch test1 usb_cam.launch

$roslaunch test1 face_detector.launch

$rqt_image_view //也可用rviz订阅

正面效果 

侧面效果

物体追踪 

程序如下，使用方式同人脸识别，在scripts目录放入程序，在launch目录放入launch文件

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
import cv2
import numpy as np
from sensor_msgs.msg import Image, RegionOfInterest
from cv_bridge import CvBridge, CvBridgeError

class motionDetector:
    def __init__(self):
        rospy.on_shutdown(self.cleanup);

        # 创建cv_bridge
        self.bridge = CvBridge()
        self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)

        # 设置参数：最小区域、阈值
        self.minArea   = rospy.get_param("~minArea",   500)
        self.threshold = rospy.get_param("~threshold", 25)

        self.firstFrame = None
        self.text = "Unoccupied"

        # 初始化订阅rgb格式图像数据的订阅者，此处图像topic的话题名可以在launch文件中重映射
        self.image_sub = rospy.Subscriber("input_rgb_image", Image, self.image_callback, queue_size=1)

    def image_callback(self, data):
        # 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式
        try:
            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")     
            frame = np.array(cv_image, dtype=np.uint8)
        except CvBridgeError, e:
            print e

        # 创建灰度图像
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (21, 21), 0)

        # 使用两帧图像做比较，检测移动物体的区域
        if self.firstFrame is None:
            self.firstFrame = gray
            return  
        frameDelta = cv2.absdiff(self.firstFrame, gray)
        thresh = cv2.threshold(frameDelta, self.threshold, 255, cv2.THRESH_BINARY)[1]

        thresh = cv2.dilate(thresh, None, iterations=2)
        binary, cnts, hierarchy= cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        for c in cnts:
            # 如果检测到的区域小于设置值，则忽略
            if cv2.contourArea(c) < self.minArea:
               continue 

            # 在输出画面上框出识别到的物体
            (x, y, w, h) = cv2.boundingRect(c)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (50, 255, 50), 2)
            self.text = "Occupied"

        # 在输出画面上打当前状态和时间戳信息
        cv2.putText(frame, "Status: {}".format(self.text), (10, 20),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

        # 将识别后的图像转换成ROS消息并发布
        self.image_pub.publish(self.bridge.cv2_to_imgmsg(frame, "bgr8"))

    def cleanup(self):
        print "Shutting down vision node."
        cv2.destroyAllWindows()

if __name__ == '__main__':
    try:
        # 初始化ros节点
        rospy.init_node("motion_detector")
        rospy.loginfo("motion_detector node is started...")
        rospy.loginfo("Please subscribe the ROS image.")
        motionDetector()
        rospy.spin()
    except KeyboardInterrupt:
        print "Shutting down motion detector node."
        cv2.destroyAllWindows()

launch

<launch>
    <node pkg="test1" name="motion_detector" type="motion_detector.py" output="screen">
        <remap from="input_rgb_image" to="/usb_cam/image_raw" />
        <rosparam>
            minArea: 500
            threshold: 25
        </rosparam>
    </node>
</launch>

执行效果

被识别出的物体有篮球，柜子，手臂，衣袖，被识别物体用边框圈出