ROS——HSV video stream threshold adjustment node (python)

Because the robot needs to realize the line-following function, it is necessary to extract the track line, so the HSV threshold adjustment of the lane line is required. Based on the local HSV adjustment code, it is added to ROS to make a node.

ROS HSV threshold regulator node

It can be adjusted in real time for the video stream, which is very convenient.

#!/usr/bin/env python
# coding:utf-8

import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError

import cv2
import numpy as np
import time
"""
功能：读取摄像头图像，显示出来，转化为HSV色彩空间
     并通过滑块调节HSV阈值，实时显示
"""

def h_low(value): 
    hsv_low[0] = value
def h_high(value): 
    hsv_high[0] = value
def s_low(value): 
    hsv_low[1] = value
def s_high(value): 
    hsv_high[1] = value
def v_low(value): 
    hsv_low[2] = value
def v_high(value): 
    hsv_high[2] = value

hsv_low = np.array([0, 0, 0])
hsv_high = np.array([0, 0, 0])

cv2.namedWindow('image')
cv2.createTrackbar('H low', 'image', 0, 255, h_low) # 初始值， 最大值
cv2.createTrackbar('H high', 'image', 0, 255, h_high)
cv2.createTrackbar('S low', 'image', 0, 255, s_low)
cv2.createTrackbar('S high', 'image', 0, 255, s_high)
cv2.createTrackbar('V low', 'image', 0, 255, v_low)
cv2.createTrackbar('V high', 'image', 0, 255, v_high)

def callback(data):
    global bridge, cv_img, flag
    flag = True
    cv_img = bridge.imgmsg_to_cv2(data, "bgr8")
    
if __name__ == '__main__':
    flag = False
    cv_img = np.zeros((640, 480))
    rospy.init_node('hsv_slider_node', anonymous=True)
    bridge = CvBridge()
    rospy.Subscriber('/image_view/image_raw', Image, callback)
    
    while not rospy.is_shutdown():
        if flag == True:
            cv2.imshow("frame", cv_img)
            hsv_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2HSV) # BGR转HSV
            hsv_img = cv2.inRange(hsv_img, hsv_low, hsv_high) # 通过HSV的高低阈值，提取图像部分区域
            cv2.imshow('hsv_img', hsv_img)
            cv2.waitKey(1)
            flag = False

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