blob: 4a67b23db01dea32352864e03b27d4204f06c66c [file] [log] [blame]
#!/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 warnings
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]
if not psnr1 or not psnr2:
return 0.0
psnr1 = [100.0 if x == float('inf') else x for x in psnr1]
psnr2 = [100.0 if x == float('inf') else x for x in psnr2]
log_rate1 = map(lambda x: math.log(x), rate1)
log_rate2 = map(lambda x: math.log(x), rate2)
try:
# 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)
except (TypeError, ZeroDivisionError, ValueError, numpy.RankWarning) as e:
return 0
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]
if not psnr1 or not psnr2:
return 0.0
psnr1 = [100.0 if x == float('inf') else x for x in psnr1]
psnr2 = [100.0 if x == float('inf') else x for x in psnr2]
log_rate1 = map(lambda x: math.log(x), rate1)
log_rate2 = map(lambda x: math.log(x), rate2)
try:
# 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)
except (TypeError, ZeroDivisionError, ValueError, numpy.RankWarning) as e:
return 0
# 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 the first char of the first word on the line is a digit
if len(line) == 0:
return False
if len(line.split()) == 0:
return False
if line.split()[0][0:1].isdigit():
return True
return False
def GetMetrics(file_name):
metric_file = open(file_name, "r")
return metric_file.readline().split();
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'] = ''
# 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))
metrics = GetMetrics(baseline_dir + "/" + dir_list[0])
metrics_js = 'metrics = ["' + '", "'.join(metrics) + '"];'
for column in range(1, len(metrics)):
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" }
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(columns_order=["file"]+dirs) + "\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)