new_contents: #!/usr/bin/python
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  # Copyright (c) 2013, Juergen Sturm, TUM
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  # 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
  
  
  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 name
      
      Output:
      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) tuples
      second_list -- second dictionary of (stamp,data) tuples
      offset -- time offset between both dictionaries (e.g., to model the delay between the sensors)
      max_difference -- search radius for candidate generation
  
      Output:
      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 matches
  
  if __name__ == '__main__':
      
      # parse command line
      parser = 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()
  
      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])))
              
          
  
subject: Create associate.py