基于ROS完成寻迹运动

安装opencv功能包：

$ sudo apt-get install ros-indigo-version-opencv libopencv-dev python-opencv

 检测指示线：

#!/usr/bin/env python
# coding=utf-8

import rospy
from sensor_msgs.msg import Image
import cv2, cv_bridge
import numpy
from geometry_msgs.msg import Twist


class Follower:
    def __init__(self):
        self.bridge = cv_bridge.CvBridge()
        cv2.namedWindow("window", 1)
        # 订阅usb摄像头
        self.image_sub = rospy.Subscriber("/usb_cam/image_raw", Image, self.image_callback)
        # self.image_sub = rospy.Subscriber("cv_bridge_image", Image, self.image_callback)
        # 订阅深度相机
        # self.image_sub = rospy.Subscriber("/camera/rgb/image_raw", Image, self.image_callback)
        # self.image_sub = rospy.Subscriber("/camera/depth/image_raw", Image, self.image_callback)
　　　　  
        self.cmd_vel_pub = rospy.Publisher("cmd_vel", Twist, queue_size=1)
        self.twist = Twist()

    def image_callback(self, msg):
        image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
        # hsv将RGB图像分解成色调H，饱和度S，明度V
        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        # 颜色的范围        # 第二个参数：lower指的是图像中低于这个lower的值，图像值变为0
        # 第三个参数：upper指的是图像中高于这个upper的值，图像值变为0
        # 而在lower～upper之间的值变成255
        lower_black = numpy.array([0, 0, 0])
        upper_black = numpy.array([50, 50, 50])
        mask = cv2.inRange(hsv, lower_black, upper_black)
        masked = cv2.bitwise_and(image, image, mask=mask)

        # 在图像某处绘制一个指示，因为只考虑20行宽的图像，所以使用numpy切片将以外的空间区域清空
        h, w, d = image.shape
        search_top = 3*h/4
        search_bot = search_top + 20
        mask[0:search_top, 0:w] = 0
        mask[search_bot:h, 0:w] = 0
        # 计算mask图像的重心，即几何中心
        M = cv2.moments(mask)
        if M['m00'] > 0:
            cx = int(M['m10']/M['m00'])
            cy = int(M['m01']/M['m00'])
            cv2.circle(image, (cx, cy), 20, (0, 0, 255), -1)
　　　　　　　　
            if not cv2.circle:
                self.twist.linear.x = -0.5
                self.twist.angular.z = 0.1
            # 计算图像中心线和目标指示线中心的距离
            erro = cx - w/2
            self.twist.linear.x = 0.5
            # self.twist.angular.z = -float(erro)/100
            self.twist.angular.z = 0

            self.cmd_vel_pub.publish(self.twist)
        cv2.imshow("window", image)
        cv2.waitKey(3)


rospy.init_node("opencv")
follower = Follower()
rospy.spin()

　　使用opencv的cvtColor()函数将RGB图像转换成HSV图像，使用numpy在HSV颜色空间中创建一个所需的色调范围，然后用inRange()函数根据色调范围生成一个二值图像。

　　跟踪指示线的策略：只考虑图像1/3高处的20行宽的部分，在检测的图像中心绘制一个圆点用来标记。

 

 

转载于:https://www.cnblogs.com/dingyc/p/10677045.html