Ubuntu下kinect v2制作数据集

1.下载kinectv2-dataset_make

git clone https://github.com/MRwangmaomao/KinectV2_dataset_make.git

下载后放在catkin_ws/src下面
2.修改保存地址
在深度图像以及RGB图像的保存 get_image.cpp需要修改保存文件的地址：
string save_path = “/home/xxxx/kinectdata”; //根据自己需要修改，这是存储数据集的文件夹的路径,在建立存储数据集kinectdata的文件夹的时候需要在文件夹下建立两文件名为depth、rgb的文件夹，这样才会保存图像文件在这两个文件夹下面。不然就只会生成两个txt文件，而不会保存图像文件。
然后保存，并编译：

cd catkin_ws
catkin_make

3.运行

roslaunch kinect2_bridge kinect2_bridge.launch rosrun dataset_make get_image_node

生成数据集

在数据集文件下执行

python associate.py rgb.txt depth.txt >associate.txt

注：首先需要去下载的源代码中将associate.py文件复制到存放数据集的文件中去，与rgb.txt depth.txt在同一文件夹下同级目录中。

associate.py代码如下

#!/usr/bin/python
# -*- coding:utf-8 -*-# RGB和深图像时间戳对齐
# 再和 groundtruth 相机轨迹对齐
# 用法 python2 associate.py rgb.txt depth.txt > associate.txt
# python associate.py associate.txt groundtruth.txt > associate_with_groundtruth.txt# Software License Agreement (BSD License)
#
# Copyright (c) 2013, Juergen Sturm, TUM
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
#  * Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  * Redistributions in binary form must reproduce the above
#    copyright notice, this list of conditions and the following
#    disclaimer in the documentation and/or other materials provided
#    with the distribution.
#  * Neither the name of TUM nor the names of its
#    contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# Requirements:
# sudo apt-get install python-argparse"""
The Kinect provides the color and depth images in an un-synchronized way.
This means that the set of time stamps from the color images do not intersect with those of the depth images.
Therefore, we need some way of associating color images to depth images.For this purpose, you can use the ''associate.py'' script.
It reads the time stamps from the rgb.txt file and the depth.txt file,
and joins them by finding the best matches."""import argparse
import sys
import os
import numpy# 读取轨迹文件=================
def read_file_list(filename):"""Reads a trajectory from a text file. File format:The file format is "stamp d1 d2 d3 ...", where stamp denotes the time stamp (to be matched)and "d1 d2 d3.." is arbitary data (e.g., a 3D position and 3D orientation) associated to this timestamp. Input:filename -- File nameOutput:dict -- dictionary of (stamp,data) tuples"""file = open(filename)data = file.read()lines = data.replace(","," ").replace("\t"," ").split("\n") list = [[v.strip() for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]list = [(float(l[0]),l[1:]) for l in list if len(l)>1]return dict(list)def associate(first_list, second_list,offset,max_difference):"""Associate two dictionaries of (stamp,data). As the time stamps never match exactly, we aim to find the closest match for every input tuple.Input:first_list -- first dictionary of (stamp,data) tuplessecond_list -- second dictionary of (stamp,data) tuplesoffset -- time offset between both dictionaries (e.g., to model the delay between the sensors)max_difference -- search radius for candidate generationOutput:matches -- list of matched tuples ((stamp1,data1),(stamp2,data2))"""first_keys = first_list.keys()second_keys = second_list.keys()potential_matches = [(abs(a - (b + offset)), a, b) for a in first_keys for b in second_keys if abs(a - (b + offset)) < max_difference]potential_matches.sort()matches = []for diff, a, b in potential_matches:if a in first_keys and b in second_keys:first_keys.remove(a)second_keys.remove(b)matches.append((a, b))matches.sort()return matchesif __name__ == '__main__':# parse command lineparser = argparse.ArgumentParser(description='''This script takes two data files with timestamps and associates them   ''')parser.add_argument('first_file', help='first text file (format: timestamp data)')parser.add_argument('second_file', help='second text file (format: timestamp data)')parser.add_argument('--first_only', help='only output associated lines from first file', action='store_true')parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)args = parser.parse_args()# 读取文件first_list = read_file_list(args.first_file)second_list = read_file_list(args.second_file)matches = associate(first_list, second_list,float(args.offset),float(args.max_difference))    if args.first_only:for a,b in matches:print("%f %s"%(a," ".join(first_list[a])))else:for a,b in matches:print("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b])))

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