OpenCV实现机器人对物体进行移动跟随的方法实例

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 kernel

capture = 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 = 50

while 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 = 0
 maxindex = 0
 for i, c in enumerate(contours):
 area = cv.contourarea(c)
 if area > maxarea:
 maxarea = area
 maxindex = 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 twist


def shutdown():
 twist = twist()
 twist.linear.x = 0
 twist.angular.z = 0
 cmd_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 kernel

 capture = 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 = 50

 while 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 = 0
 maxindex = 0
 for i, c in enumerate(contours):
 area = cv.contourarea(c)
 if area > maxarea:
 maxarea = area
 maxindex = 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 = 0.1
 twist.angular.z = 0.5
 print "turn left"
 elif screen_center - offset <= center_x <= screen_center + offset:
 twist.linear.x = 0.3
 twist.angular.z = 0
 print "keep"
 elif center_x > screen_center + offset:
 twist.linear.x = 0.1
 twist.angular.z = -0.5
 print "turn right"
 else:
 twist.linear.x = 0
 twist.angular.z = 0
 print "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()

总结

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