ros小车运动时红绿灯识别功能代码

红绿灯识别是一个比较复杂的功能，需要涉及到图像处理、机器学习等技术。以下是一个简单的基于ROS的红绿灯识别功能代码：

1. 安装必要的软件包

sudo apt-get install ros-kinetic-image-transport
sudo apt-get install ros-kinetic-cv-bridge
sudo apt-get install ros-kinetic-vision-opencv

2. 创建ROS节点

roscore

3. 订阅摄像头图像

rosrun image_transport republish compressed in:=/camera/image_raw raw out:=/camera/image_raw

4. 使用OpenCV进行图像处理

#!/usr/bin/env python
import rospy
from cv_bridge import CvBridge, CvBridgeError
from sensor_msgs.msg import Image
import cv2

class TrafficLightDetector:
    def __init__(self):
        self.bridge = CvBridge()
        self.image_sub = rospy.Subscriber('/camera/image_raw', Image, self.callback)

    def callback(self, data):
        try:
            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
        except CvBridgeError as e:
            print(e)

        # 在这里添加你的红绿灯识别代码

if __name__ == '__main__':
    rospy.init_node('traffic_light_detector')
    detector = TrafficLightDetector()
    rospy.spin()

5. 添加红绿灯识别代码

#!/usr/bin/env python
import rospy
from cv_bridge import CvBridge, CvBridgeError
from sensor_msgs.msg import Image
import cv2

class TrafficLightDetector:
    def __init__(self):
        self.bridge = CvBridge()
        self.image_sub = rospy.Subscriber('/camera/image_raw', Image, self.callback)

    def callback(self, data):
        try:
            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
        except CvBridgeError as e:
            print(e)

        # 将图像转换为灰度图像
        gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)

        # 将灰度图像进行二值化
        ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

        # 找出所有的轮廓
        contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        # 遍历所有的轮廓
        for cnt in contours:
            # 计算轮廓的面积
            area = cv2.contourArea(cnt)

            # 如果面积小于一定值，则忽略该轮廓
            if area < 1000:
                continue

            # 计算轮廓的周长
            perimeter = cv2.arcLength(cnt, True)

            # 使用近似多边形来拟合轮廓
            approx = cv2.approxPolyDP(cnt, 0.03 * perimeter, True)

            # 如果检测到4个点，则认为是红绿灯
            if len(approx) == 4:
                cv2.drawContours(cv_image, [approx], 0, (0, 255, 0), 2)

        cv2.imshow("Traffic Light Detector", cv_image)
        cv2.waitKey(1)

if __name__ == '__main__':
    rospy.init_node('traffic_light_detector')
    detector = TrafficLightDetector()
    rospy.spin()

6. 运行ROS节点

rosrun <package_name> <node_name>

其中 `<package_name>` 是你的ROS包的名称，`<node_name>` 是你的ROS节点的名称。

参考资料：
- [ROS OpenCV Tutorial - Traffic Light Detector](https://github.com/ros-perception/opencv_tutorials/blob/master/traffic_light_detector/scripts/traffic_light_detector.py)