机器人对物体进行移动跟随

• 1.物体识别
• 2.移动跟随

1.物体识别

本案例实现对特殊颜色物体的识别，并实现根据物体位置的改变进行控制跟随。

import cv2 as cv# 定义结构元素
kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
# print kernelcapture = cv.VideoCapture(0)		
print capture.isOpened()
ok, frame = capture.read()
lower_b = (65, 43, 46)
upper_b = (110, 255, 255)height, width = frame.shape[0:2]
screen_center = width / 2
offset = 50while ok:# 将图像转成HSV颜色空间hsv_frame = cv.cvtColor(frame, cv.COLOR_BGR2HSV)# 基于颜色的物体提取mask = cv.inRange(hsv_frame, lower_b, upper_b)mask2 = cv.morphologyEx(mask, cv.MORPH_OPEN, kernel)mask3 = cv.morphologyEx(mask2, cv.MORPH_CLOSE, kernel)# 找出面积最大的区域_, contours, _ = cv.findContours(mask3, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)maxArea = 0maxIndex = 0for i, c in enumerate(contours):area = cv.contourArea(c)if area > maxArea:maxArea = areamaxIndex = i# 绘制cv.drawContours(frame, contours, maxIndex, (255, 255, 0), 2)# 获取外切矩形x, y, w, h = cv.boundingRect(contours[maxIndex])cv.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)# 获取中心像素点center_x = int(x + w/2)center_y = int(y + h/2)cv.circle(frame, (center_x, center_y), 5, (0, 0, 255), -1)# 简单的打印反馈数据，之后补充运动控制if center_x < screen_center - offset:print "turn left"elif screen_center - offset <= center_x <= screen_center + offset:print "keep"elif center_x > screen_center + offset:print "turn right"cv.imshow("mask4", mask3)cv.imshow("frame", frame)cv.waitKey(1)ok, frame = capture.read()

实际效果图

2.移动跟随

结合ROS控制turtlebot3或其他机器人运动，turtlebot3机器人的教程见我另一个博文：ROS控制Turtlebot3

首先启动turtlebot3，如下代码可以放在机器人的树莓派中，将相机插在USB口即可

代码示例：

import rospy
import cv2 as cv
from geometry_msgs.msg import Twistdef shutdown():twist = Twist()twist.linear.x = 0twist.angular.z = 0cmd_vel_Publisher.publish(twist)print "stop"if __name__ == '__main__':rospy.init_node("follow_node")rospy.on_shutdown(shutdown)rate = rospy.Rate(100)cmd_vel_Publisher = rospy.Publisher("/cmd_vel", Twist, queue_size=1)# 定义结构元素kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))# print kernelcapture = cv.VideoCapture(0)print capture.isOpened()ok, frame = capture.read()lower_b = (65, 43, 46)upper_b = (110, 255, 255)height, width = frame.shape[0:2]screen_center = width / 2offset = 50while not rospy.is_shutdown():# 将图像转成HSV颜色空间hsv_frame = cv.cvtColor(frame, cv.COLOR_BGR2HSV)# 基于颜色的物体提取mask = cv.inRange(hsv_frame, lower_b, upper_b)mask2 = cv.morphologyEx(mask, cv.MORPH_OPEN, kernel)mask3 = cv.morphologyEx(mask2, cv.MORPH_CLOSE, kernel)# 找出面积最大的区域_, contours, _ = cv.findContours(mask3, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)maxArea = 0maxIndex = 0for i, c in enumerate(contours):area = cv.contourArea(c)if area > maxArea:maxArea = areamaxIndex = i# 绘制cv.drawContours(frame, contours, maxIndex, (255, 255, 0), 2)# 获取外切矩形x, y, w, h = cv.boundingRect(contours[maxIndex])cv.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)# 获取中心像素点center_x = int(x + w / 2)center_y = int(y + h / 2)cv.circle(frame, (center_x, center_y), 5, (0, 0, 255), -1)# 简单的打印反馈数据，之后补充运动控制twist = Twist()if center_x < screen_center - offset:twist.linear.x = 0.1twist.angular.z = 0.5print "turn left"elif screen_center - offset <= center_x <= screen_center + offset:twist.linear.x = 0.3twist.angular.z = 0print "keep"elif center_x > screen_center + offset:twist.linear.x = 0.1twist.angular.z = -0.5print "turn right"else:twist.linear.x = 0twist.angular.z = 0print "stop"# 将速度发出cmd_vel_Publisher.publish(twist)# cv.imshow("mask4", mask3)# cv.imshow("frame", frame)cv.waitKey(1)rate.sleep()ok, frame = capture.read()

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