OpenCV實(shí)現(xiàn)機(jī)器人對(duì)物體進(jìn)行移動(dòng)跟隨的方法實(shí)例

更新時(shí)間：2020年11月09日 10:37:20 作者：Arcann

這篇文章主要給大家介紹了關(guān)于OpenCV實(shí)現(xiàn)機(jī)器人對(duì)物體進(jìn)行移動(dòng)跟隨的相關(guān)資料，文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

1.物體識(shí)別

本案例實(shí)現(xiàn)對(duì)特殊顏色物體的識(shí)別，并實(shí)現(xiàn)根據(jù)物體位置的改變進(jìn)行控制跟隨。

import cv2 as cv

# 定義結(jié)構(gòu)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:
 # 將圖像轉(zhuǎn)成HSV顏色空間
 hsv_frame = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
 # 基于顏色的物體提取
 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)
 
 # 找出面積最大的區(qū)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
	# 繪制
 cv.drawContours(frame, contours, maxIndex, (255, 255, 0), 2)
 # 獲取外切矩形
 x, y, w, h = cv.boundingRect(contours[maxIndex])
 cv.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
 # 獲取中心像素點(diǎn)
 center_x = int(x + w/2)
 center_y = int(y + h/2)
 cv.circle(frame, (center_x, center_y), 5, (0, 0, 255), -1)

 # 簡(jiǎn)單的打印反饋數(shù)據(jù)，之后補(bǔ)充運(yùn)動(dòng)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()

實(shí)際效果圖

2.移動(dòng)跟隨

結(jié)合ROS控制turtlebot3或其他機(jī)器人運(yùn)動(dòng)，turtlebot3機(jī)器人的教程見我另一個(gè)博文：ROS控制Turtlebot3

首先啟動(dòng)turtlebot3，如下代碼可以放在機(jī)器人的樹莓派中，將相機(jī)插在USB口即可

代碼示例：

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)
 # 定義結(jié)構(gòu)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():
 # 將圖像轉(zhuǎn)成HSV顏色空間
 hsv_frame = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
 # 基于顏色的物體提取
 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)

 # 找出面積最大的區(qū)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
 # 繪制
 cv.drawContours(frame, contours, maxIndex, (255, 255, 0), 2)
 # 獲取外切矩形
 x, y, w, h = cv.boundingRect(contours[maxIndex])
 cv.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
 # 獲取中心像素點(diǎn)
 center_x = int(x + w / 2)
 center_y = int(y + h / 2)
 cv.circle(frame, (center_x, center_y), 5, (0, 0, 255), -1)

 # 簡(jiǎn)單的打印反饋數(shù)據(jù)，之后補(bǔ)充運(yùn)動(dòng)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"

 # 將速度發(fā)出
 cmd_vel_Publisher.publish(twist)

 # cv.imshow("mask4", mask3)
 # cv.imshow("frame", frame)
 cv.waitKey(1)
 rate.sleep()
 ok, frame = capture.read()

總結(jié)

到此這篇關(guān)于OpenCV實(shí)現(xiàn)機(jī)器人對(duì)物體進(jìn)行移動(dòng)跟隨的文章就介紹到這了,更多相關(guān)OpenCV機(jī)器人對(duì)物體移動(dòng)跟隨內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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