ROS+OpenCV 人脸识别，物体识别

比眉伴天荒 2023-02-17 11:03 45阅读 0赞

**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 test2
    $mkdir launch scripts

### 3.人脸识别程序 ###

> $cd test2/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 test2/launch
> 
> $touch usb\_cam.launch face\_detector.launch

usb\_cam.launch开启摄像头

face\_detector.launch运行人脸识别程序

<launch>
        <node pkg="test2" 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 test2 usb\_cam.launch
> 
> $roslaunch test2 face\_detector.launch
> 
> $rqt\_image\_view //也可用rviz订阅

正面效果

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3_size_16_color_FFFFFF_t_70][]

侧面效果

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3_size_16_color_FFFFFF_t_70 1][]

## 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="test2" 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

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3_size_16_color_FFFFFF_t_70 2][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/2020061313484038.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200613135003214.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20200613135340534.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NrNzg0MTAxNzc3,size_16,color_FFFFFF,t_70