Move aom source to a sub-directory under media/libaom

There is no damned reason to treat this differently than any other media lib given its license and there never was.

1 files changed, 466 insertions, 0 deletions

diff --git a/media/libaom/src/test/visual_metrics.py b/media/libaom/src/test/visual_metrics.py
new file mode 100644
index 000000000..9055feb33
--- /dev/null
+++ b/media/libaom/src/test/visual_metrics.py
@@ -0,0 +1,466 @@
+#!/usr/bin/python
+#
+# Copyright (c) 2016, Alliance for Open Media. All rights reserved
+#
+# This source code is subject to the terms of the BSD 2 Clause License and
+# the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
+# was not distributed with this source code in the LICENSE file, you can
+# obtain it at www.aomedia.org/license/software. If the Alliance for Open
+# Media Patent License 1.0 was not distributed with this source code in the
+# PATENTS file, you can obtain it at www.aomedia.org/license/patent.
+#
+
+"""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 as np
+import scipy as sp
+import scipy.interpolate
+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
+
+warnings.simplefilter('ignore', np.RankWarning)
+warnings.simplefilter('ignore', RuntimeWarning)
+
+def bdsnr2(metric_set1, metric_set2):
+  """
+  BJONTEGAARD    Bjontegaard metric calculation adapted
+  Bjontegaard's snr metric allows to compute the average % saving in decibels
+  between two rate-distortion curves [1].  This is an adaptation of that
+  method that fixes inconsistencies when the curve fit operation goes awry
+  by replacing the curve fit function with a Piecewise Cubic Hermite
+  Interpolating Polynomial and then integrating that by evaluating that
+  function at small intervals using the trapezoid method to calculate
+  the integral.
+
+  metric_set1 - list of tuples ( bitrate,  metric ) for first graph
+  metric_set2 - list of tuples ( bitrate,  metric ) for second graph
+  """
+
+  if not metric_set1 or not metric_set2:
+    return 0.0
+
+  try:
+
+    # pchip_interlopate requires keys sorted by x axis. x-axis will
+    # be our metric not the bitrate so sort by metric.
+    metric_set1.sort()
+    metric_set2.sort()
+
+    # Pull the log of the rate and clamped psnr from metric_sets.
+    log_rate1 = [math.log(x[0]) for x in metric_set1]
+    metric1 = [100.0 if x[1] == float('inf') else x[1] for x in metric_set1]
+    log_rate2 = [math.log(x[0]) for x in metric_set2]
+    metric2 = [100.0 if x[1] == float('inf') else x[1] for x in metric_set2]
+
+    # Integration interval.  This metric only works on the area that's
+    # overlapping.   Extrapolation of these things is sketchy so we avoid.
+    min_int = max([min(log_rate1), min(log_rate2)])
+    max_int = min([max(log_rate1), max(log_rate2)])
+
+    # No overlap means no sensible metric possible.
+    if max_int <= min_int:
+      return 0.0
+
+    # Use Piecewise Cubic Hermite Interpolating Polynomial interpolation to
+    # create 100 new samples points separated by interval.
+    lin = np.linspace(min_int, max_int, num=100, retstep=True)
+    interval = lin[1]
+    samples = lin[0]
+    v1 = scipy.interpolate.pchip_interpolate(log_rate1, metric1, samples)
+    v2 = scipy.interpolate.pchip_interpolate(log_rate2, metric2, samples)
+
+    # Calculate the integral using the trapezoid method on the samples.
+    int_v1 = np.trapz(v1, dx=interval)
+    int_v2 = np.trapz(v2, dx=interval)
+
+    # Calculate the average improvement.
+    avg_exp_diff = (int_v2 - int_v1) / (max_int - min_int)
+
+  except (TypeError, ZeroDivisionError, ValueError, np.RankWarning) as e:
+    return 0
+
+  return avg_exp_diff
+
+def bdrate2(metric_set1, metric_set2):
+  """
+  BJONTEGAARD    Bjontegaard metric calculation adapted
+  Bjontegaard's metric allows to compute the average % saving in bitrate
+  between two rate-distortion curves [1].  This is an adaptation of that
+  method that fixes inconsistencies when the curve fit operation goes awry
+  by replacing the curve fit function with a Piecewise Cubic Hermite
+  Interpolating Polynomial and then integrating that by evaluating that
+  function at small intervals using the trapezoid method to calculate
+  the integral.
+
+  metric_set1 - list of tuples ( bitrate,  metric ) for first graph
+  metric_set2 - list of tuples ( bitrate,  metric ) for second graph
+  """
+
+  if not metric_set1 or not metric_set2:
+    return 0.0
+
+  try:
+
+    # pchip_interlopate requires keys sorted by x axis. x-axis will
+    # be our metric not the bitrate so sort by metric.
+    metric_set1.sort(key=lambda tup: tup[1])
+    metric_set2.sort(key=lambda tup: tup[1])
+
+    # Pull the log of the rate and clamped psnr from metric_sets.
+    log_rate1 = [math.log(x[0]) for x in metric_set1]
+    metric1 = [100.0 if x[1] == float('inf') else x[1] for x in metric_set1]
+    log_rate2 = [math.log(x[0]) for x in metric_set2]
+    metric2 = [100.0 if x[1] == float('inf') else x[1] for x in metric_set2]
+
+    # Integration interval.  This metric only works on the area that's
+    # overlapping.   Extrapolation of these things is sketchy so we avoid.
+    min_int = max([min(metric1), min(metric2)])
+    max_int = min([max(metric1), max(metric2)])
+
+    # No overlap means no sensible metric possible.
+    if max_int <= min_int:
+      return 0.0
+
+    # Use Piecewise Cubic Hermite Interpolating Polynomial interpolation to
+    # create 100 new samples points separated by interval.
+    lin = np.linspace(min_int, max_int, num=100, retstep=True)
+    interval = lin[1]
+    samples = lin[0]
+    v1 = scipy.interpolate.pchip_interpolate(metric1, log_rate1, samples)
+    v2 = scipy.interpolate.pchip_interpolate(metric2, log_rate2, samples)
+
+    # Calculate the integral using the trapezoid method on the samples.
+    int_v1 = np.trapz(v1, dx=interval)
+    int_v2 = np.trapz(v2, dx=interval)
+
+    # Calculate the average improvement.
+    avg_exp_diff = (int_v2 - int_v1) / (max_int - min_int)
+
+  except (TypeError, ZeroDivisionError, ValueError, np.RankWarning) as e:
+    return 0
+
+  # 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 aomenc 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):
+        try:
+          tuple = float(metrics[0]), float(metrics[metric_column])
+        except:
+          tuple = float(metrics[0]), 0
+      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:
+      if bitrate == 0:
+        continue
+      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)
+
+          if estimated_s2_bitrate == 0:
+            continue
+          # 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 = bdsnr2(metric_set1_sorted, metric_set2_sorted)
+  else:
+      avg_improvement = bdrate2(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 AOM 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" aom aom_b aom_c > 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)