visual_metrics.py - webm/vpx_codec_comparison - Git at Google

 #!/usr/bin/python
 #
 # Copyright 2010 Google Inc.
 # All Rights Reserved.

 """Converts video encoding result data from text files to visualization
 data source."""

 __author__ = "jzern@google.com (James Zern),"
 __author__ += "jimbankoski@google.com (Jim Bankoski)"

 import fnmatch
 import numpy
 import os
 import re
 import string
 import sys
 import math

 import gviz_api

 from os.path import basename
 from os.path import splitext

 def bdsnr(metric_set1, metric_set2):
   """
   BJONTEGAARD    Bjontegaard metric calculation
   Bjontegaard's metric allows to compute the average gain in psnr between two
   rate-distortion curves [1].
   rate1,psnr1 - RD points for curve 1
   rate2,psnr2 - RD points for curve 2

   returns the calculated Bjontegaard metric 'dsnr'

   code adapted from code written by : (c) 2010 Giuseppe Valenzise
   http://www.mathworks.com/matlabcentral/fileexchange/27798-bjontegaard-metric/content/bjontegaard.m
   """
   rate1 = [x[0] for x in metric_set1]
   psnr1 = [x[1] for x in metric_set1]
   rate2 = [x[0] for x in metric_set2]
   psnr2 = [x[1] for x in metric_set2]

   log_rate1 = map(lambda x: math.log(x), rate1)
   log_rate2 = map(lambda x: math.log(x), rate2)

   # Best cubic poly fit for graph represented by log_ratex, psrn_x.
   p1 = numpy.polyfit(log_rate1, psnr1, 3)
   p2 = numpy.polyfit(log_rate2, psnr2, 3)

   # Integration interval.
   min_int = max([min(log_rate1),min(log_rate2)])
   max_int = min([max(log_rate1),max(log_rate2)])

   # Integrate p1, and p2.
   p_int1 = numpy.polyint(p1)
   p_int2 = numpy.polyint(p2)

   # Calculate the integrated value over the interval we care about.
   int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
   int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

   # Calculate the average improvement.
   avg_diff = (int2 - int1) / (max_int - min_int)
   return avg_diff

 def bdrate(metric_set1, metric_set2):
   """
   BJONTEGAARD    Bjontegaard metric calculation
   Bjontegaard's metric allows to compute the average % saving in bitrate
   between two rate-distortion curves [1].

   rate1,psnr1 - RD points for curve 1
   rate2,psnr2 - RD points for curve 2

   adapted from code from: (c) 2010 Giuseppe Valenzise

   """
   rate1 = [x[0] for x in metric_set1]
   psnr1 = [x[1] for x in metric_set1]
   rate2 = [x[0] for x in metric_set2]
   psnr2 = [x[1] for x in metric_set2]

   log_rate1 = map(lambda x: math.log(x), rate1)
   log_rate2 = map(lambda x: math.log(x), rate2)

   # Best cubic poly fit for graph represented by log_ratex, psrn_x.
   p1 = numpy.polyfit(psnr1, log_rate1, 3)
   p2 = numpy.polyfit(psnr2, log_rate2, 3)

   # Integration interval.
   min_int = max([min(psnr1),min(psnr2)])
   max_int = min([max(psnr1),max(psnr2)])

   # find integral
   p_int1 = numpy.polyint(p1)
   p_int2 = numpy.polyint(p2)

   # Calculate the integrated value over the interval we care about.
   int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
   int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

   # Calculate the average improvement.
   avg_exp_diff = (int2 - int1) / (max_int - min_int)

   # In really bad formed data the exponent can grow too large.
   # clamp it.
   if avg_exp_diff > 200 :
     avg_exp_diff = 200

   # Convert to a percentage.
   avg_diff = (math.exp(avg_exp_diff) - 1) * 100

   return avg_diff


 def FillForm(string_for_substitution, dictionary_of_vars):
   """
   This function substitutes all matches of the command string //%% ... %%//
   with the variable represented by ...  .
   """
   return_string = string_for_substitution
   for i in re.findall("//%%(.*)%%//", string_for_substitution):
     return_string = re.sub("//%%" + i + "%%//", dictionary_of_vars[i],
                            return_string)
   return return_string


 def HasMetrics(line):
   """
   The metrics files produced by vpxenc are started with a B for headers.
   """
   if line[0:1] != "B" and len(string.split(line))>0:
     return True
   return False


 def ParseMetricFile(file_name, metric_column):
   metric_set1 = set([])
   metric_file = open(file_name, "r")
   for line in metric_file:
     metrics = string.split(line)
     if HasMetrics(line):
       if metric_column < len(metrics):
         tuple = float(metrics[0]), float(metrics[metric_column])
       else:
         tuple = float(metrics[0]), 0
       metric_set1.add(tuple)
   metric_set1_sorted = sorted(metric_set1)
   return metric_set1_sorted


 def FileBetter(file_name_1, file_name_2, metric_column, method):
   """
   Compares two data files and determines which is better and by how
   much. Also produces a histogram of how much better, by PSNR.
   metric_column is the metric.
   """
   # Store and parse our two files into lists of unique tuples.

   # Read the two files, parsing out lines starting with bitrate.
   metric_set1_sorted = ParseMetricFile(file_name_1, metric_column)
   metric_set2_sorted = ParseMetricFile(file_name_2, metric_column)


   def GraphBetter(metric_set1_sorted, metric_set2_sorted, base_is_set_2):
     """
     Search through the sorted metric file for metrics on either side of
     the metric from file 1.  Since both lists are sorted we really
     should not have to search through the entire range, but these
     are small files."""
     total_bitrate_difference_ratio = 0.0
     count = 0
     for bitrate, metric in metric_set1_sorted:
       for i in range(len(metric_set2_sorted) - 1):
         s2_bitrate_0, s2_metric_0 = metric_set2_sorted[i]
         s2_bitrate_1, s2_metric_1 = metric_set2_sorted[i + 1]
         # We have a point on either side of our metric range.
         if metric > s2_metric_0 and metric <= s2_metric_1:

           # Calculate a slope.
           if s2_metric_1 - s2_metric_0 != 0:
             metric_slope = ((s2_bitrate_1 - s2_bitrate_0) /
                             (s2_metric_1 - s2_metric_0))
           else:
             metric_slope = 0

           estimated_s2_bitrate = (s2_bitrate_0 + (metric - s2_metric_0) *
                                   metric_slope)

           # Calculate percentage difference as given by base.
           if base_is_set_2 == 0:
             bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
                                         bitrate)
           else:
             bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
                                         estimated_s2_bitrate)

           total_bitrate_difference_ratio += bitrate_difference_ratio
           count += 1
           break

     # Calculate the average improvement between graphs.
     if count != 0:
       avg = total_bitrate_difference_ratio / count

     else:
       avg = 0.0

     return avg

   # Be fair to both graphs by testing all the points in each.
   if method == 'avg':
     avg_improvement = 50 * (
                        GraphBetter(metric_set1_sorted, metric_set2_sorted, 1) -
                        GraphBetter(metric_set2_sorted, metric_set1_sorted, 0))
   elif method == 'dsnr':
       avg_improvement = bdsnr(metric_set1_sorted, metric_set2_sorted)
   else:
       avg_improvement = bdrate(metric_set2_sorted, metric_set1_sorted)

   return avg_improvement


 def HandleFiles(variables):
   """
   This script creates html for displaying metric data produced from data
   in a video stats file,  as created by the WEBM project when enable_psnr
   is turned on:

   Usage: visual_metrics.py template.html pattern base_dir sub_dir [ sub_dir2 ..]

   The script parses each metrics file [see below] that matches the
   statfile_pattern  in the baseline directory and looks for the file that
   matches that same file in each of the sub_dirs, and compares the resultant
   metrics bitrate, avg psnr, glb psnr, and ssim. "

   It provides a table in which each row is a file in the line directory,
   and a column for each subdir, with the cells representing how that clip
   compares to baseline for that subdir.   A graph is given for each which
   compares filesize to that metric.  If you click on a point in the graph it
   zooms in on that point.

   a SAMPLE metrics file:

   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
    25.911   38.242   38.104   38.258   38.121   75.790    14103
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
    49.982   41.264   41.129   41.255   41.122   83.993    19817
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
    74.967   42.911   42.767   42.899   42.756   87.928    17332
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
   100.012   43.983   43.838   43.881   43.738   89.695    25389
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
   149.980   45.338   45.203   45.184   45.043   91.591    25438
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
   199.852   46.225   46.123   46.113   45.999   92.679    28302
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
   249.922   46.864   46.773   46.777   46.673   93.334    27244
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
   299.998   47.366   47.281   47.317   47.220   93.844    27137
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
   349.769   47.746   47.677   47.722   47.648   94.178    32226
   Bitrate  AVGPsnr  GLBPsnr  AVPsnrP  GLPsnrP  VPXSSIM    Time(us)
   399.773   48.032   47.971   48.013   47.946   94.362    36203

   sample use:
   visual_metrics.py template.html "*stt" vp8 vp8b vp8c > metrics.html
   """

   # The template file is the html file into which we will write the
   # data from the stats file, formatted correctly for the gviz_api.
   template_file = open(variables[1], "r")
   page_template = template_file.read()
   template_file.close()

   # This is the path match pattern for finding stats files amongst
   # all the other files it could be.  eg: *.stt
   file_pattern = variables[2]

   # This is the directory with files that we will use to do the comparison
   # against.
   baseline_dir = variables[3]
   snrs = ''
   filestable = {}
   filestable['dsnr'] = ''
   filestable['drate'] = ''
   filestable['avg'] = ''

   # Go through each metric in the list.
   for column in range(1,2):

     # Dirs is directories after the baseline to compare to the base.
     dirs = variables[4:len(variables)]

     # Find the metric files in the baseline directory.
     dir_list = sorted(fnmatch.filter(os.listdir(baseline_dir), file_pattern))

     for metric in ['avg','dsnr','drate']:
       description = {"file": ("string", "File")}

       # Go through each directory and add a column header to our description.
       countoverall = {}
       sumoverall = {}

       for directory in dirs:
         description[directory] = ("number", directory)
         countoverall[directory] = 0
         sumoverall[directory] = 0

       # Data holds the data for the visualization, name given comes from
       # gviz_api sample code.
       data = []
       for filename in dir_list:
         row = {'file': splitext(basename(filename))[0] }
         baseline_file_name = baseline_dir + "/" + filename

         # Read the metric file from each of the directories in our list.
         for directory in dirs:
           metric_file_name = directory + "/" + filename

           # If there is a metric file in the current directory, open it
           # and calculate its overall difference between it and the baseline
           # directory's metric file.
           if os.path.isfile(metric_file_name):
             overall = FileBetter(baseline_file_name, metric_file_name,
                                  column, metric)
             row[directory] = overall

             sumoverall[directory] += overall
             countoverall[directory] += 1

         data.append(row)

       # Add the overall numbers.
       row = {"file": "OVERALL" }
       if countoverall[directory]:
         for directory in dirs:
           row[directory] = sumoverall[directory] / countoverall[directory]
       data.append(row)

       # write the tables out
       data_table = gviz_api.DataTable(description)
       data_table.LoadData(data)

       filestable[metric] = ( filestable[metric] + "filestable_" + metric +
                              "[" + str(column) + "]=" + data_table.ToJSon()
                              + "\n" )

     filestable_avg = filestable['avg']
     filestable_dpsnr = filestable['dsnr']
     filestable_drate = filestable['drate']

     # Now we collect all the data for all the graphs.  First the column
     # headers which will be Datarate and then each directory.
     columns = ("datarate",baseline_dir)
     description = {"datarate":("number", "Datarate")}
     for directory in dirs:
       description[directory] = ("number", directory)

     description[baseline_dir] = ("number", baseline_dir)

     snrs = snrs + "snrs[" + str(column) + "] = ["

     # Now collect the data for the graphs, file by file.
     for filename in dir_list:

       data = []

       # Collect the file in each directory and store all of its metrics
       # in the associated gviz metrics table.
       all_dirs = dirs + [baseline_dir]
       for directory in all_dirs:

         metric_file_name = directory + "/" + filename
         if not os.path.isfile(metric_file_name):
           continue

         # Read and parse the metrics file storing it to the data we'll
         # use for the gviz_api.Datatable.
         metrics = ParseMetricFile(metric_file_name, column)
         for bitrate, metric in metrics:
           data.append({"datarate": bitrate, directory: metric})

       data_table = gviz_api.DataTable(description)
       data_table.LoadData(data)
       snrs = snrs + "'" + data_table.ToJSon(
          columns_order=tuple(["datarate",baseline_dir]+dirs)) + "',"

     snrs = snrs + "]\n"

     formatters = ""
     for i in range(len(dirs)):
       formatters = "%s   formatter.format(better, %d);" % (formatters, i+1)

   print FillForm(page_template, vars())
   return

 if len(sys.argv) < 3:
   print HandleFiles.__doc__
 else:
   HandleFiles(sys.argv)
	#!/usr/bin/python
	#
	# Copyright 2010 Google Inc.
	# All Rights Reserved.

	"""Converts video encoding result data from text files to visualization
	data source."""

	__author__ = "jzern@google.com (James Zern),"
	__author__ += "jimbankoski@google.com (Jim Bankoski)"

	import fnmatch
	import numpy
	import os
	import re
	import string
	import sys
	import math

	import gviz_api

	from os.path import basename
	from os.path import splitext

	def bdsnr(metric_set1, metric_set2):
	"""
	BJONTEGAARD Bjontegaard metric calculation
	Bjontegaard's metric allows to compute the average gain in psnr between two
	rate-distortion curves [1].
	rate1,psnr1 - RD points for curve 1
	rate2,psnr2 - RD points for curve 2

	returns the calculated Bjontegaard metric 'dsnr'

	code adapted from code written by : (c) 2010 Giuseppe Valenzise
	http://www.mathworks.com/matlabcentral/fileexchange/27798-bjontegaard-metric/content/bjontegaard.m
	"""
	rate1 = [x[0] for x in metric_set1]
	psnr1 = [x[1] for x in metric_set1]
	rate2 = [x[0] for x in metric_set2]
	psnr2 = [x[1] for x in metric_set2]

	log_rate1 = map(lambda x: math.log(x), rate1)
	log_rate2 = map(lambda x: math.log(x), rate2)

	# Best cubic poly fit for graph represented by log_ratex, psrn_x.
	p1 = numpy.polyfit(log_rate1, psnr1, 3)
	p2 = numpy.polyfit(log_rate2, psnr2, 3)

	# Integration interval.
	min_int = max([min(log_rate1),min(log_rate2)])
	max_int = min([max(log_rate1),max(log_rate2)])

	# Integrate p1, and p2.
	p_int1 = numpy.polyint(p1)
	p_int2 = numpy.polyint(p2)

	# Calculate the integrated value over the interval we care about.
	int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
	int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

	# Calculate the average improvement.
	avg_diff = (int2 - int1) / (max_int - min_int)
	return avg_diff

	def bdrate(metric_set1, metric_set2):
	"""
	BJONTEGAARD Bjontegaard metric calculation
	Bjontegaard's metric allows to compute the average % saving in bitrate
	between two rate-distortion curves [1].

	rate1,psnr1 - RD points for curve 1
	rate2,psnr2 - RD points for curve 2

	adapted from code from: (c) 2010 Giuseppe Valenzise

	"""
	rate1 = [x[0] for x in metric_set1]
	psnr1 = [x[1] for x in metric_set1]
	rate2 = [x[0] for x in metric_set2]
	psnr2 = [x[1] for x in metric_set2]

	log_rate1 = map(lambda x: math.log(x), rate1)
	log_rate2 = map(lambda x: math.log(x), rate2)

	# Best cubic poly fit for graph represented by log_ratex, psrn_x.
	p1 = numpy.polyfit(psnr1, log_rate1, 3)
	p2 = numpy.polyfit(psnr2, log_rate2, 3)

	# Integration interval.
	min_int = max([min(psnr1),min(psnr2)])
	max_int = min([max(psnr1),max(psnr2)])

	# find integral
	p_int1 = numpy.polyint(p1)
	p_int2 = numpy.polyint(p2)

	# Calculate the integrated value over the interval we care about.
	int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
	int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

	# Calculate the average improvement.
	avg_exp_diff = (int2 - int1) / (max_int - min_int)

	# In really bad formed data the exponent can grow too large.
	# clamp it.
	if avg_exp_diff > 200 :
	avg_exp_diff = 200

	# Convert to a percentage.
	avg_diff = (math.exp(avg_exp_diff) - 1) * 100

	return avg_diff


	def FillForm(string_for_substitution, dictionary_of_vars):
	"""
	This function substitutes all matches of the command string //%% ... %%//
	with the variable represented by ... .
	"""
	return_string = string_for_substitution
	for i in re.findall("//%%(.*)%%//", string_for_substitution):
	return_string = re.sub("//%%" + i + "%%//", dictionary_of_vars[i],
	return_string)
	return return_string


	def HasMetrics(line):
	"""
	The metrics files produced by vpxenc are started with a B for headers.
	"""
	if line[0:1] != "B" and len(string.split(line))>0:
	return True
	return False


	def ParseMetricFile(file_name, metric_column):
	metric_set1 = set([])
	metric_file = open(file_name, "r")
	for line in metric_file:
	metrics = string.split(line)
	if HasMetrics(line):
	if metric_column < len(metrics):
	tuple = float(metrics[0]), float(metrics[metric_column])
	else:
	tuple = float(metrics[0]), 0
	metric_set1.add(tuple)
	metric_set1_sorted = sorted(metric_set1)
	return metric_set1_sorted


	def FileBetter(file_name_1, file_name_2, metric_column, method):
	"""
	Compares two data files and determines which is better and by how
	much. Also produces a histogram of how much better, by PSNR.
	metric_column is the metric.
	"""
	# Store and parse our two files into lists of unique tuples.

	# Read the two files, parsing out lines starting with bitrate.
	metric_set1_sorted = ParseMetricFile(file_name_1, metric_column)
	metric_set2_sorted = ParseMetricFile(file_name_2, metric_column)


	def GraphBetter(metric_set1_sorted, metric_set2_sorted, base_is_set_2):
	"""
	Search through the sorted metric file for metrics on either side of
	the metric from file 1. Since both lists are sorted we really
	should not have to search through the entire range, but these
	are small files."""
	total_bitrate_difference_ratio = 0.0
	count = 0
	for bitrate, metric in metric_set1_sorted:
	for i in range(len(metric_set2_sorted) - 1):
	s2_bitrate_0, s2_metric_0 = metric_set2_sorted[i]
	s2_bitrate_1, s2_metric_1 = metric_set2_sorted[i + 1]
	# We have a point on either side of our metric range.
	if metric > s2_metric_0 and metric <= s2_metric_1:

	# Calculate a slope.
	if s2_metric_1 - s2_metric_0 != 0:
	metric_slope = ((s2_bitrate_1 - s2_bitrate_0) /
	(s2_metric_1 - s2_metric_0))
	else:
	metric_slope = 0

	estimated_s2_bitrate = (s2_bitrate_0 + (metric - s2_metric_0) *
	metric_slope)

	# Calculate percentage difference as given by base.
	if base_is_set_2 == 0:
	bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
	bitrate)
	else:
	bitrate_difference_ratio = ((bitrate - estimated_s2_bitrate) /
	estimated_s2_bitrate)

	total_bitrate_difference_ratio += bitrate_difference_ratio
	count += 1
	break

	# Calculate the average improvement between graphs.
	if count != 0:
	avg = total_bitrate_difference_ratio / count

	else:
	avg = 0.0

	return avg

	# Be fair to both graphs by testing all the points in each.
	if method == 'avg':
	avg_improvement = 50 * (
	GraphBetter(metric_set1_sorted, metric_set2_sorted, 1) -
	GraphBetter(metric_set2_sorted, metric_set1_sorted, 0))
	elif method == 'dsnr':
	avg_improvement = bdsnr(metric_set1_sorted, metric_set2_sorted)
	else:
	avg_improvement = bdrate(metric_set2_sorted, metric_set1_sorted)

	return avg_improvement


	def HandleFiles(variables):
	"""
	This script creates html for displaying metric data produced from data
	in a video stats file, as created by the WEBM project when enable_psnr
	is turned on:

	Usage: visual_metrics.py template.html pattern base_dir sub_dir [ sub_dir2 ..]

	The script parses each metrics file [see below] that matches the
	statfile_pattern in the baseline directory and looks for the file that
	matches that same file in each of the sub_dirs, and compares the resultant
	metrics bitrate, avg psnr, glb psnr, and ssim. "

	It provides a table in which each row is a file in the line directory,
	and a column for each subdir, with the cells representing how that clip
	compares to baseline for that subdir. A graph is given for each which
	compares filesize to that metric. If you click on a point in the graph it
	zooms in on that point.

	a SAMPLE metrics file:

	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	25.911 38.242 38.104 38.258 38.121 75.790 14103
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	49.982 41.264 41.129 41.255 41.122 83.993 19817
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	74.967 42.911 42.767 42.899 42.756 87.928 17332
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	100.012 43.983 43.838 43.881 43.738 89.695 25389
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	149.980 45.338 45.203 45.184 45.043 91.591 25438
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	199.852 46.225 46.123 46.113 45.999 92.679 28302
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	249.922 46.864 46.773 46.777 46.673 93.334 27244
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	299.998 47.366 47.281 47.317 47.220 93.844 27137
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	349.769 47.746 47.677 47.722 47.648 94.178 32226
	Bitrate AVGPsnr GLBPsnr AVPsnrP GLPsnrP VPXSSIM Time(us)
	399.773 48.032 47.971 48.013 47.946 94.362 36203

	sample use:
	visual_metrics.py template.html "*stt" vp8 vp8b vp8c > metrics.html
	"""

	# The template file is the html file into which we will write the
	# data from the stats file, formatted correctly for the gviz_api.
	template_file = open(variables[1], "r")
	page_template = template_file.read()
	template_file.close()

	# This is the path match pattern for finding stats files amongst
	# all the other files it could be. eg: *.stt
	file_pattern = variables[2]

	# This is the directory with files that we will use to do the comparison
	# against.
	baseline_dir = variables[3]
	snrs = ''
	filestable = {}
	filestable['dsnr'] = ''
	filestable['drate'] = ''
	filestable['avg'] = ''

	# Go through each metric in the list.
	for column in range(1,2):

	# Dirs is directories after the baseline to compare to the base.
	dirs = variables[4:len(variables)]

	# Find the metric files in the baseline directory.
	dir_list = sorted(fnmatch.filter(os.listdir(baseline_dir), file_pattern))

	for metric in ['avg','dsnr','drate']:
	description = {"file": ("string", "File")}

	# Go through each directory and add a column header to our description.
	countoverall = {}
	sumoverall = {}

	for directory in dirs:
	description[directory] = ("number", directory)
	countoverall[directory] = 0
	sumoverall[directory] = 0

	# Data holds the data for the visualization, name given comes from
	# gviz_api sample code.
	data = []
	for filename in dir_list:
	row = {'file': splitext(basename(filename))[0] }
	baseline_file_name = baseline_dir + "/" + filename

	# Read the metric file from each of the directories in our list.
	for directory in dirs:
	metric_file_name = directory + "/" + filename

	# If there is a metric file in the current directory, open it
	# and calculate its overall difference between it and the baseline
	# directory's metric file.
	if os.path.isfile(metric_file_name):
	overall = FileBetter(baseline_file_name, metric_file_name,
	column, metric)
	row[directory] = overall

	sumoverall[directory] += overall
	countoverall[directory] += 1

	data.append(row)

	# Add the overall numbers.
	row = {"file": "OVERALL" }
	if countoverall[directory]:
	for directory in dirs:
	row[directory] = sumoverall[directory] / countoverall[directory]
	data.append(row)

	# write the tables out
	data_table = gviz_api.DataTable(description)
	data_table.LoadData(data)

	filestable[metric] = ( filestable[metric] + "filestable_" + metric +
	"[" + str(column) + "]=" + data_table.ToJSon()
	+ "\n" )

	filestable_avg = filestable['avg']
	filestable_dpsnr = filestable['dsnr']
	filestable_drate = filestable['drate']

	# Now we collect all the data for all the graphs. First the column
	# headers which will be Datarate and then each directory.
	columns = ("datarate",baseline_dir)
	description = {"datarate":("number", "Datarate")}
	for directory in dirs:
	description[directory] = ("number", directory)

	description[baseline_dir] = ("number", baseline_dir)

	snrs = snrs + "snrs[" + str(column) + "] = ["

	# Now collect the data for the graphs, file by file.
	for filename in dir_list:

	data = []

	# Collect the file in each directory and store all of its metrics
	# in the associated gviz metrics table.
	all_dirs = dirs + [baseline_dir]
	for directory in all_dirs:

	metric_file_name = directory + "/" + filename
	if not os.path.isfile(metric_file_name):
	continue

	# Read and parse the metrics file storing it to the data we'll
	# use for the gviz_api.Datatable.
	metrics = ParseMetricFile(metric_file_name, column)
	for bitrate, metric in metrics:
	data.append({"datarate": bitrate, directory: metric})

	data_table = gviz_api.DataTable(description)
	data_table.LoadData(data)
	snrs = snrs + "'" + data_table.ToJSon(
	columns_order=tuple(["datarate",baseline_dir]+dirs)) + "',"

	snrs = snrs + "]\n"

	formatters = ""
	for i in range(len(dirs)):
	formatters = "%s formatter.format(better, %d);" % (formatters, i+1)

	print FillForm(page_template, vars())
	return

	if len(sys.argv) < 3:
	print HandleFiles.__doc__
	else:
	HandleFiles(sys.argv)