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 #!/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)