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ros2-4.2 用python实现人脸识别

article 2025/1/10 1:43:16

需求:创建一个人脸检测服务提供图像，返回人脸数量位置信息
难点分析:
1.人脸怎么识别?face_recognition

2.图片数据和结果怎么传递?服务通信就很合适
3.没有合适的消息接口?自定义一个

4.2.1 自定义服务接口

在chat4/chapt_ws/src下新创建功能包

ros2 pkg create chapt4_interfaces --dependencies rosidl_default_generators sensor_msgs --license Apache-2.0

创建好功能包，在chapt4_interfaces 下创建目录srv,在srv下面创建文件：FaceDetector.srv

内容如下

sensor_msgs/Image image  # 原始图像
---
int16 number      # 人脸个数
float32 use_time  # 识别耗时
int32[] top       # 人脸在图像中位置
int32[] right
int32[] bottom
int32[] left

---分成了两部分，上面表示请求类型是 sensor_msgs/Image的图像。下面是response部分。

然后，再CMakeLists.txt 对该接口文件进行注册，声明为接口文件，添加依赖。代码如下

cmake_minimum_required(VERSION 3.8)
project(chapt4_interfaces)

if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
  add_compile_options(-Wall -Wextra -Wpedantic)
endif()

# find dependencies
find_package(ament_cmake REQUIRED)
find_package(rosidl_default_generators REQUIRED)
find_package(sensor_msgs REQUIRED)

rosidl_generate_interfaces(${PROJECT_NAME}
  "srv/FaceDetector.srv"
  DEPENDENCIES sensor_msgs
)


if(BUILD_TESTING)
  find_package(ament_lint_auto REQUIRED)
  # the following line skips the linter which checks for copyrights
  # comment the line when a copyright and license is added to all source files
  set(ament_cmake_copyright_FOUND TRUE)
  # the following line skips cpplint (only works in a git repo)
  # comment the line when this package is in a git repo and when
  # a copyright and license is added to all source files
  set(ament_cmake_cpplint_FOUND TRUE)
  ament_lint_auto_find_test_dependencies()
endif()

ament_package()

还修改下package.xml ，添加<member_of_group>rosidl_interface_packages</member_of_group>，接下来进行构建。

bohu@bohu-TM1701:~/chapt4/chapt4_ws$ colcon build
Starting >>> chapt4_interfaces
Finished <<< chapt4_interfaces [6.51s]                     

Summary: 1 package finished [7.56s]
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ source install/setup.bash 
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ ros2 interface show chapt4_interfaces/srv/FaceDetector 
sensor_msgs/Image image  # 原始图像
        std_msgs/Header header #
                builtin_interfaces/Time stamp
                        int32 sec
                        uint32 nanosec
                string frame_id
                                     # Header frame_id should be optical frame of camera
                                     # origin of frame should be optical center of cameara
                                     # +x should point to the right in the image
                                     # +y should point down in the image
                                     # +z should point into to plane of the image
                                     # If the frame_id here and the frame_id of the CameraInfo
                                     # message associated with the image conflict
                                     # the behavior is undefined
        uint32 height                #
        uint32 width                 #
        string encoding       #
                              # taken from the list of strings in include/sensor_msgs/image_encodings.hpp
        uint8 is_bigendian    #
        uint32 step           #
        uint8[] data          #
---
int16 number      # 人脸个数
float32 use_time  # 识别耗时
int32[] top       # 人脸在图像中位置
int32[] right
int32[] bottom
int32[] left

4.2.2 人脸检测

pip3 install face_recognition -i https://pypi.tuna.tsinghua.edu.cn/simple

我安装的时候，对于依赖的dlib卡主了好几分钟。

  Building wheel for dlib (setup.py) ... 一直在等

关于 face_recognition

  • github : GitHub - ageitgey/face_recognition: The world's simplest facial recognition api for Python and the command line

可以使用Python和命令行工具提取、识别、操作人脸。应该是1.2.2版本之后不在发新版本维护了。

创建demo_python_service 功能包。

ros2 pkg create demo_python_service --build-type ament_python --dependencies rclpy chapt4_interfaces --license Apache-2.0

添加一张图片，网上找了张我喜欢的大话西游。

在setup.py增加对于图片的复制配置，默认是不会复制到install下面

data_files=[

('share/ament_index/resource_index/packages',

['resource/' + package_name]),

('share/' + package_name, ['package.xml']),

('share/' + package_name+"/resource", ['resource/default.jpg']),

],

再次构建功能包，就发现图片被复制到chapt4_ws/install/demo_python_service/目录下

/share/demo_python_service/resource 有图片。

编写代码src/demo_python_service/demo_python_service 下创建文件：learn_face_detect.py.代码如下：

import face_recognition
import cv2
from ament_index_python.packages import get_package_share_directory #获取shares目录绝对路径

def main():
    #获取图片绝对路径
    default_image_path = get_package_share_directory('demo_python_service')+'/resource/default.jpg'
    print(f"path:{default_image_path}")
    #cv2加载图片
    image = cv2.imread(default_image_path)
    #检测人脸
    face_recognitions =face_recognition.face_locations(image,number_of_times_to_upsample=1,model='hog')
    #绘制人脸边框
    for top,right,bottom,left in face_recognitions:
        cv2.rectangle(image,(left,top),(right,bottom),(255,0,0),4)
    #显示结果
    cv2.imshow('face detection',image)
    cv2.waitKey(0)

 依赖了face_recognition、cv2. 在setup.py对于learn_face_detect节点注册。构建并运行。

bohu@bohu-TM1701:~/chapt4/chapt4_ws$ colcon build
Starting >>> chapt4_interfaces
Finished <<< chapt4_interfaces [0.77s]                     
Starting >>> demo_python_service
Finished <<< demo_python_service [1.37s]          

Summary: 2 packages finished [2.52s]
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ source install/setup.bash 
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ ros2 run demo_python_service learn_face_detect
path:/home/bohu/chapt4/chapt4_ws/install/demo_python_service/share/demo_python_service/resource/default.jpg

效果如下：

4.2.3 人脸检测服务实现

在demo_python_service目录下创建face_detect_node.py 文件，代码如下：

import rclpy
from rclpy.node import Node
from chapt4_interfaces.srv import FaceDetector
import face_recognition
import cv2
from ament_index_python.packages import get_package_share_directory #获取shares目录绝对路径
from cv_bridge import CvBridge #用于图片格式转换
import time

class FaceDetectorionNode(Node):
    def __init__(self):
        super().__init__('face_detection_node')
        self.service_ = self.create_service(FaceDetector,'face_detect',self.detect_face_callback)
        self.bridge = CvBridge();
        self.number_of_times_to_upsample =1;
        self.model = "hog"
        self.default_image_path = get_package_share_directory('demo_python_service')+'/resource/default.jpg'
        self.get_logger().info("人脸检测服务已经启动")

    def detect_face_callback(self,request,response):
        if request.image.data:
            cv_image = self.bridge.imgmsg_to_cv2(request.image)
        else:
            cv_image = cv2.imread(self.default_image_path) #没有图像用默认兜底
            self.get_logger().info(f"传入图像为空，使用默认图像。")  
         #cv_image 已经是一个opencv格式的图像
        start_time = time.time()
        self.get_logger().info(f"加载完成图像，开始识别")  
         #检测人脸
        face_recognitions =face_recognition.face_locations(cv_image,
        number_of_times_to_upsample=self.number_of_times_to_upsample,model=self.model)
        response.use_time = time.time() -start_time
        response.number = len(face_recognitions)
        for top,right,bottom,left in face_recognitions:
            response.top.append(top)
            response.right.append(right)
            response.bottom.append(bottom)
            response.left.append(left)


        return response    #必须返回response

def main(args=None):
    rclpy.init(args=args)
    node = FaceDetectorionNode()
    rclpy.spin(node)
    rclpy.shutdown()

注意的是，使用了CvBridge 进行图片格式（从ros2的图像到opencv格式）的转换。

       在代码FaceDetectorionNode 类中继承了Node，并在初始化__init__方法中调用create_service创建了一个服务，三个参数：FaceDetector 是消息接口，第二个face_detect是服务名，第三个detect_face_callback是回调函数。主要逻辑处理在回调函数。response需要返回，其余逻辑跟上一节类似，图像格式转换，进行检测，对response数据赋值。

   接着在setup.py对face_detect_node节点进行注册，然后构建和运行。

bohu@bohu-TM1701:~/chapt4/chapt4_ws$ colcon build
Starting >>> chapt4_interfaces
Finished <<< chapt4_interfaces [0.64s]                     
Starting >>> demo_python_service
Finished <<< demo_python_service [1.17s]          

Summary: 2 packages finished [2.16s]
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ ros2 run demo_python_service face_detect_node 
[INFO] [1736320005.105902282] [face_detection_node]: 人脸检测服务已经启动
[INFO] [1736320181.732996097] [face_detection_node]: 传入图像为空，使用默认图像。
[INFO] [1736320181.733454024] [face_detection_node]: 加载完成图像，开始识别

新开终端,查看服务列表并调用

bohu@bohu-TM1701:~/chapt4/chapt4_ws$ ros2 service list -t
/face_detect [chapt4_interfaces/srv/FaceDetector]
/face_detection_node/describe_parameters [rcl_interfaces/srv/DescribeParameters]
/face_detection_node/get_parameter_types [rcl_interfaces/srv/GetParameterTypes]
/face_detection_node/get_parameters [rcl_interfaces/srv/GetParameters]
/face_detection_node/list_parameters [rcl_interfaces/srv/ListParameters]
/face_detection_node/set_parameters [rcl_interfaces/srv/SetParameters]
/face_detection_node/set_parameters_atomically [rcl_interfaces/srv/SetParametersAtomically]
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ source install/setup.bash 
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ ros2 service call /face_detect chapt4_interfaces/srv/FaceDetector
requester: making request: chapt4_interfaces.srv.FaceDetector_Request(image=sensor_msgs.msg.Image(header=std_msgs.msg.Header(stamp=builtin_interfaces.msg.Time(sec=0, nanosec=0), frame_id=''), height=0, width=0, encoding='', is_bigendian=0, step=0, data=[]))

response:
chapt4_interfaces.srv.FaceDetector_Response(number=2, use_time=0.2230970859527588, top=[217, 112], right=[687, 438], bottom=[440, 379], left=[464, 171])

4.2.4 人脸检测客户端实现

再次换一张图片，我这次换了电影海报，人脸多一些。放到resouce下，叫做2.jpg

setup.py也要加一下新图片复制。

接着src/demo_python_service/demo_python_service 下创建文件:face_detect_client_node.py

代码如下：

import rclpy
from rclpy.node import Node
from chapt4_interfaces.srv import FaceDetector
import face_recognition
import cv2
from ament_index_python.packages import get_package_share_directory #获取shares目录绝对路径
from cv_bridge import CvBridge #用于图片格式转换
import time

class FaceDetectClientNode(Node):
    def __init__(self):
        super().__init__('face_detect_client_node')
        self.bridge = CvBridge();
        self.default_image_path = get_package_share_directory('demo_python_service')+'/resource/2.jpg'
        self.get_logger().info("人脸检测客户端已经启动")
        self.client = self.create_client(FaceDetector,'face_detect')
        self.image = cv2.imread(self.default_image_path) 

    def send_request(self):
        #1 判断服务端是否在线
        while self.client.wait_for_service(timeout_sec=1.0) is False:
            self.get_logger().info('等待服务端上线')
        #2构造request
        request =  FaceDetector.Request()
        request.image = self.bridge.cv2_to_imgmsg(self.image)
        #3 发送请求并等待处理完成
        future = self.client.call_async(request) 
        #现在future 并没有包含结果，需要等待服务端处理完成,不能直接sleep
        #rclpy.spin_until_future_complete(self,future)
        def reslut_callback(result_future):
            response =result_future.result() #获取响应
            self.get_logger().info(f'响应检测到{response.number}张脸，耗时{response.use_time}')
            self.show_respnse(response)
        future.add_done_callback(reslut_callback)    

    def show_respnse(self,response):
        for i in range(response.number):
            top = response.top[i]
            right = response.right[i]
            bottom = response.bottom[i]
            left = response.left[i]
            cv2.rectangle(self.image, (left, top),
                        (right, bottom), (255, 0, 0), 2)
            
        cv2.imshow('人脸识别result:',self.image)
        cv2.waitKey(0) #也是阻塞的


def main(args=None):
    rclpy.init(args=args)
    node = FaceDetectClientNode()
    node.send_request()
    rclpy.spin(node)
    rclpy.shutdown()

处理显示图像跟之前差不多，send_request比较巧妙的一点是可以使用异步请求，返回的future没有包含结果，需要等服务端处理完成。这里书上例子采用rclpy.spin_until_future_complete 来等待异步返回结果，视频里小鱼老师用回调函数又演示了一版。

还是要在setup.py注册face_detect_client_node 节点，接着构建。

先启动服务端 ros2 run demo_python_service face_detect_node，再运行客户端

bohu@bohu-TM1701:~/chapt4/chapt4_ws$ colcon build
Starting >>> chapt4_interfaces
Finished <<< chapt4_interfaces [0.71s]
Starting >>> demo_python_service
Finished <<< demo_python_service [1.40s]          

Summary: 2 packages finished [2.47s]
bohu@bohu-TM1701:~/chapt4/chapt4_ws$ ros2 run demo_python_service face_detect_client_node 
[INFO] [1736326208.922806215] [face_detect_client_node]: 人脸检测客户端已经启动
[INFO] [1736326209.369307885] [face_detect_client_node]: 响应检测到8张脸，耗时0.34261298179626465

识别效果还是不错的。


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