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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