OpenCV实现机器人对物体进行移动跟随
2020-11-06 14:38
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机器人对物体进行移动跟随
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 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()
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