1.打开相机

cd camera_ws
source devel/setup.bash
roslaunch usb_cam usb_cam-dual_model_rectified.launch

2.可见光相机畸变矫正

安装相关库

catkin_make -DCATKIN_WHITELIST_PACKAGES="aruco;aruco_ros;aruco_msgs"
catkin_make -DCATKIN_WHITELIST_PACKAGES="aruco_mapping;lidar_camera_calibration"
catkin_make -DCATKIN_WHITELIST_PACKAGES=""

查看相机参数

ls /dev/video* #查看video个数和名称
v4l2-ctl -d 0 --all #查看video0的参数

修改launch文件

<param name="video_device" value="/dev/video1" />
<param name="image_width" value="640" />
<param name="image_height" value="480" />
<param name="pixel_format" value="mjpeg" />

启动标定程序

rosrun camera_calibration cameracalibrator.py --size 8x6 --square 0.06 image:=/usb_cam/image_raw camera:=/usb_cam

移动标定板时，标定窗口中的各指标增加。
当CALIBRATE按钮亮起时，单击计算相机内参矩阵及矫正参数，并输出至终端，此过程可能需要1-2分钟。
当SAVE按钮亮起时，单击将采集的样本图片和计算结果保存
当COMMIT按钮亮起时，单击将标定结果写入~/.ros/camera_info/head_camera.yaml。注：此标定文件将被原始图像节点usb_cam读取，标定参数会被发布至话题/usb_cam/camera_info以供矫正图像节点image_proc使用。

启动矫正图像节点

ROS_NAMESPACE=/cam_rgb/usb_cam rosrun image_proc image_proc

3.查看相机图像

rqt_image_view

4.计算H举矩阵

手动标注获取每一组的H矩阵（h.py）

#!/usr/bin/python
# -*- coding: UTF-8 -*-
from PIL import Image
import cv2
import numpy as np
import pylab as pl
import matplotlib.pyplot as plt
import scipy.io as sio
from PIL import Imagedef re_img(path):img = cv2.imread(path)a=int(20) #x startb=int(700) #x endc=int(0) #y startd=int(510) #y endcropimg=img[c:d,a:b]imgresize=cv2.resize(cropimg,(640,480))return imgresizeif __name__ == '__main__' :num = 9h=np.zeros((3,3))# 读取红外图.inf_Fig = "./rgb1/" + str(num) +"_dual.png"img_dst = re_img(inf_Fig)pl.figure(), pl.imshow(img_dst[:, :, ::-1]), pl.title('dual')# 红外图中的4个点dst_point = plt.ginput(4)  dst_point = np.float32(dst_point)# 读取RGB图像.RGB_Fig_1 = "./rgb1/"+ str(num) +".png"im_src_1 = cv2.imread(RGB_Fig_1)pl.figure(), pl.imshow(im_src_1[:, :, ::-1]), pl.title('rgb1')# RGB图中的4个点src_point_1 = plt.ginput(4)  src_point_1 = np.float32(src_point_1)# Calculate Homographyh, status = cv2.findHomography(src_point_1, dst_point)# Warp source image to destination based on homographyim_out_1 = cv2.warpPerspective(im_src_1,h,(640,480),borderValue=(255,255,255))pl.imshow(im_out_1[:, :, ::-1]), pl.title('out')pl.show()  #show dst#保存H矩阵H1_name = 'h'+str(num)+'.mat'print('H1:',h)sio.savemat(H1_name, {'Homography_Mat_1':h})

计算出平均H矩阵并查看标定效果（MeanH_Test.py）

#!/usr/bin/python
# -*- coding: UTF-8 -*-from PIL import Image
import cv2
import numpy as np
#import pylab as pl
#import matplotlib.pyplot as plt
import scipy.io as sio
import timeclass bd():def get_time_stamp(self):ct = time.time()local_time = time.localtime(ct)data_head = time.strftime("%Y-%m-%d %H:%M:%S", local_time)data_secs = (ct - int(ct)) * 1000time_stamp = "%s.%03d" % (data_head, data_secs)#print(time_stamp)stamp = ("".join(time_stamp.split()[0].split("-"))+"".join(time_stamp.split()[1].split(":"))).replace('.', '')#print(stamp)return stampdef re_img(self,img):#img = cv2.imread(path)a=int(20) #x startb=int(700) #x endc=int(0) #y startd=int(510) #y endcropimg=img[c:d,a:b]imgresize=cv2.resize(cropimg,(640,480))return imgresizedef H_use(self,fig1):name_1 = 'H11.mat'data = sio.loadmat(name_1)h1 = data['Homography_Mat_1']h1[0][2]=h1[0][2]+30 #根据实际效果的修正h1[1][2]=h1[1][2]+10#print(h1[0][2])#print(h1)im_out_1 = cv2.warpPerspective(fig1,h1,(640,480))return im_out_1def Htest(self,path1,path2):im_src_1 = cv2.imread(path1)img_dst = self.re_img(path2)im_out_1= self.H_use(im_src_1)#pl.figure(),pl.imshow(img_dst[:, :, ::-1]), pl.title('dst'),#pl.figure(),pl.imshow(im_out_1[:, :, ::-1]), pl.title('out1'),#pl.show()return im_out_1, img_dstdef Htest_t(self,img_t):img_dst = self.re_img(img_t)return img_dstdef Htest_rgb(self,img_rgb):im_out_1= self.H_use(img_rgb)return im_out_1def cal_H(self):num=0H=np.zeros((3,3))while int(num) < 10:print(num)name = 'h' + str(num) +'.mat'data=sio.loadmat(name)h = data['Homography_Mat_1']print("h:",h)num = num + 1H=H+hH=H/10H1_name = 'H.mat'print('H1:',H)sio.savemat(H1_name, {'Homography_Mat_1':H})if __name__ == '__main__' :B=bd()num=0#B.cal_H()while int(num)<10:rgb_path="./rgb1/"+str(num)+".png"  t_path="./rgb1/"+str(num)+"_t.png"img_rgb,img_t = B.Htest(rgb_path,t_path)cv2.imwrite('./out/'+str(num)+'.png',img_rgb)cv2.imwrite('./out/'+str(num)+'_t.png',img_t)num=num+1time=B.get_time_stamp()print(time)

5.接受、处理、发送图像（biaoding.py）

#! /usr/bin/env python2from __future__ import print_function#python2
import os
import roslib
import rospy
from std_msgs.msg import Header
from std_msgs.msg import String
from sensor_msgs.msg import Image
# from ros_numpy import msgify
from cv_bridge import CvBridge
import sys
sys.path.remove('/opt/ros/melodic/lib/python2.7/dist-packages')
import threading
import time
import numpy as np
import MeanH_Test as biao
import cv2
import threading
#python3class SubscribeAndPublish:def __init__(self):self.all_obstacle_str=''self.sub1_name="/cam_rgb/usb_cam/image_rect_color"#self.sub1_name="/cam_rgb/usb_cam/image_raw"self.sub1= rospy.Subscriber(self.sub1_name, Image,self.callback_rgb)self.sub2_name="/cam_t/usb_cam/image_raw"self.sub2= rospy.Subscriber(self.sub2_name, Image,self.callback_t)self.rate = 10self.timer = rospy.Timer(rospy.Duration(1.0 / self.rate), self.callback_timer)self.lock_rgb = threading.Lock()self.lock_t = threading.Lock()self.img_rgb_global = []self.img_t_global = []self.pub1_name="pub_rgb"self.pub1= rospy.Publisher(self.pub1_name, Image,queue_size=1)self.pub2_name="pub_t"self.pub2= rospy.Publisher(self.pub2_name, Image,queue_size=1)self.mode=0self.path_rgb='./img/img_rgb'self.path_t='./img/img_t'# self.bridge = CvBridge()def callback_rgb(self,data):print('callback1')self.lock_rgb.acquire()img = np.frombuffer(data.data, dtype=np.uint8).reshape(data.height, data.width, -1)if len(self.img_rgb_global) > 0:self.img_rgb_global.pop(0)self.img_rgb_global.append(img)else:self.img_rgb_global.append(img)self.lock_rgb.release()#print('rgb')#img = CvBridge().imgmsg_to_cv2(data, "bgr8")#img = np.frombuffer(data.data, dtype=np.uint8).reshape(data.height, data.width, -1)#print(img.shape)#B=biao.bd()#time=B.get_time_stamp()#name='rgb_'+str(time)+'.jpg'#img_rgb=B.Htest_rgb(img)#cv2.imwrite('./img/img_rgb/'+str(name),img_rgb)#self.pub1.publish(CvBridge().cv2_to_imgmsg(img_rgb,"bgr8"))def callback_t(self,data):print('callback2')self.lock_t.acquire()img = np.frombuffer(data.data, dtype=np.uint8).reshape(data.height, data.width, -1)if len(self.img_t_global) > 0:self.img_t_global.pop(0)self.img_t_global.append(img)else:self.img_t_global.append(img)self.lock_t.release()#print('t')#img = CvBridge().imgmsg_to_cv2(data, "bgr8")#img = np.frombuffer(data.data, dtype=np.uint8).reshape(data.height, data.width, -1)#print(img.shape)#B=biao.bd()#time=B.get_time_stamp()#name='t_'+str(time)+'.jpg'#img_t=B.Htest_t(img)#cv2.imwrite('./img/img_t/'+str(name),img_t)#  self.pub2.publish(CvBridge().cv2_to_imgmsg(img_t,"bgr8"))def callback_timer(self, event):print('callback')self.lock_rgb.acquire()if len(self.img_rgb_global) > 0:img_rgb = self.img_rgb_global[0].copy()else:returnself.lock_rgb.release()self.lock_t.acquire()if len(self.img_t_global) > 0:img_t = self.img_t_global[0].copy()else:returnself.lock_t.release()B=biao.bd()time=B.get_time_stamp()img_rgb=B.Htest_rgb(img_rgb)img_t=B.Htest_t(img_t)img_rgb=img_rgb[:,:,::-1]img_t=img_t[:,:,::-1]self.mode=modeif self.mode==0:self.path_rgb=path_rgbself.path_t=path_tcv2.imwrite(self.path_rgb+'/rgb_'+str(time)+'.jpg',img_rgb)cv2.imwrite(self.path_t+'/t_'+str(time)+'.jpg',img_t)print('save image successful!')self.pub1.publish(CvBridge().cv2_to_imgmsg(img_rgb,"rgb8"))self.pub2.publish(CvBridge().cv2_to_imgmsg(img_t,"rgb8"))def main(mode,path_rgb,path_t):rospy.init_node('biaoding_ws', anonymous=True)#####################t=SubscribeAndPublish()#####################rospy.spin()
if __name__ == "__main__":mode=0if len(sys.argv)>1:mode=int(sys.argv[1])if mode>1:mode=1else:mode=0print(mode)if mode==0:print("save image")else:print("no save image")#create dirsb=biao.bd()ti=b.get_time_stamp()path='./img'+str(ti)folder=os.path.exists(path)if not folder:os.makedirs(path)path_rgb=path+'/img_rgb'os.makedirs(path_rgb)path_t=path+'/img_t'os.makedirs(path_t)main(mode,path_rgb,path_t)

6.标定效果

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可见光相机与红外相机标定

1.打开相机

2.可见光相机畸变矫正

安装相关库

查看相机参数

修改launch文件

启动标定程序

启动矫正图像节点

3.查看相机图像

4.计算H举矩阵

手动标注获取每一组的H矩阵（h.py）

计算出平均H矩阵并查看标定效果（MeanH_Test.py）

5.接受、处理、发送图像（biaoding.py）

6.标定效果

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