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compute_BD_rates.py
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compute_BD_rates.py
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import sqlite3
import sys
import logging
import ntpath
from statistics import mean
from collections import namedtuple
from collections import defaultdict
from bd_rate_calculator import BDrateCalculator
from analyze_encoding_results import apply_size_check
__author__ = "Aditya Mavlankar"
__copyright__ = "Copyright 2019-2020, Netflix, Inc."
__credits__ = ["Kyle Swanson", "Jan de Cock", "Marjan Parsa"]
__license__ = "Apache License, Version 2.0"
__version__ = "0.1"
__maintainer__ = "Aditya Mavlankar"
__email__ = "[email protected]"
__status__ = "Development"
RateQualityPoint = namedtuple('RateQualityPoint', ['bpp', 'quality', 'target_metric', 'target_value'])
BD_RATE_EXCEPTION_STRING = 'BD_RATE_EXCEPTION'
def get_unique_sources_sorted(connection):
unique_sources = connection.execute('SELECT DISTINCT SOURCE FROM ENCODES').fetchall()
unique_sources = [elem[0] for elem in unique_sources]
return sorted(list(set(unique_sources)))
def get_quality_dict(elem, list_of_metrics):
quality = dict()
for index, metric in enumerate(list_of_metrics):
quality[metric] = elem[index]
return quality
def get_rate_quality_points(connection, sub_sampling, codec, source, total_pixels, list_of_metrics):
# print('{} {} {}'.format(codec, sub_sampling, source))
csv_metrics_upper = ','.join([elem.upper() for elem in list_of_metrics])
points = connection.execute("SELECT {},FILE_SIZE_BYTES,TARGET_METRIC,TARGET_VALUE FROM ENCODES WHERE CODEC='{}' AND SUB_SAMPLING='{}' AND SOURCE='{}'"
.format(csv_metrics_upper, codec, sub_sampling, source)).fetchall()
rate_quality_points = [
RateQualityPoint(elem[len(list_of_metrics)] * 8 / total_pixels, get_quality_dict(elem, list_of_metrics),
elem[len(list_of_metrics) + 1], elem[len(list_of_metrics) + 2]) for elem in points]
# print(repr(rate_quality_points))
return rate_quality_points
def get_rates(rate_quality_points):
return [rate_quality_point.bpp for rate_quality_point in rate_quality_points]
def get_quality(rate_quality_points, metric):
return [rate_quality_point.quality[metric] for rate_quality_point in rate_quality_points]
def get_formatted_bdrate(val):
if isinstance(val, str):
return val
else:
return '{:.2f}'.format(val).rjust(6)
def get_formatted_mean_bdrate(val):
return '{:.2f}'.format(val).rjust(22)
def my_shorten(name, width):
return (name[:width - 3] + '...') if len(name) > width else name
def print_bd_rates(bdrates_various_metrics, codec, unique_sources, black_list_source_various_metrics,
list_of_metrics):
bdrates_this_codec_various_metrics = dict()
for metric in list_of_metrics:
bdrates_this_codec_various_metrics[metric] = list()
max_len_source_name = len(max(unique_sources, key=len))
max_len_to_use_for_printing = min(80, max_len_source_name)
for source in unique_sources:
print_string = ' {} {}'.format(my_shorten(source,
max_len_to_use_for_printing)
.ljust(max_len_to_use_for_printing),
codec)
for metric in list_of_metrics:
print_string += ' BDRate-{} {}'.format(metric.upper(), get_formatted_bdrate(bdrates_various_metrics[metric][codec][source]))
if source not in black_list_source_various_metrics[metric]:
bdrates_this_codec_various_metrics[metric].append(bdrates_various_metrics[metric][codec][source])
print(print_string)
result = codec.ljust(16)
result_local = result
for metric in list_of_metrics:
result += '{}'.format(get_formatted_mean_bdrate(mean(bdrates_this_codec_various_metrics[metric])))
result_local += ' Mean BDRate-{} {:.2f}'.format(metric.upper(), mean(bdrates_this_codec_various_metrics[metric]))
print(result_local + '\n')
return result
def main(argv):
db_file_name = 'encoding_results_vmaf.db'
if len(argv) > 0:
db_file_name = argv[0]
connection = sqlite3.connect(db_file_name)
logger = logging.getLogger('report.bdrates')
logger.addHandler(logging.FileHandler('bdrates_' + ntpath.basename(db_file_name) + '.txt'))
logger.addHandler(logging.StreamHandler())
logger.setLevel('DEBUG')
unique_sources = get_unique_sources_sorted(connection)
total_pixels = apply_size_check(connection)
baseline_codec = 'jpeg'
sub_sampling_arr = ['420', '444']
codecs = ['jpeg-mse', 'jpeg-ms-ssim', 'jpeg-im', 'jpeg-hvs-psnr', 'webp', 'kakadu-mse', 'kakadu-visual', 'openjpeg',
'hevc', 'avif-mse', 'avif-ssim']
metrics_for_BDRate = ['vmaf', 'ssim', 'ms_ssim', 'vif', 'psnr_y', 'psnr_avg']
for sub_sampling in sub_sampling_arr:
bdrates_various_metrics = dict()
black_list_source_various_metrics = dict()
for metric in metrics_for_BDRate:
bdrates_various_metrics[metric] = defaultdict(dict)
black_list_source_various_metrics[metric] = list()
print('\n\nComputing BD rates for subsampling {}'.format(sub_sampling))
for source in unique_sources:
baseline_rate_quality_points = get_rate_quality_points(connection, sub_sampling, baseline_codec, source, total_pixels, metrics_for_BDRate)
for codec in codecs:
if codec == 'webp' and sub_sampling == '444':
continue
rate_quality_points = get_rate_quality_points(connection, sub_sampling, codec, source, total_pixels, metrics_for_BDRate)
for metric in metrics_for_BDRate:
# print(metric.upper())
try:
bd_rate_val = 100.0 * BDrateCalculator.CalcBDRate(
list(zip(get_rates(baseline_rate_quality_points), get_quality(baseline_rate_quality_points, metric))),
list(zip(get_rates(rate_quality_points), get_quality(rate_quality_points, metric))))
bdrates_various_metrics[metric][codec][source] = bd_rate_val
except AssertionError as e:
print('{} {} {} {}: '.format(metric, source, codec, sub_sampling) + str(e))
bdrates_various_metrics[metric][codec][source] = BD_RATE_EXCEPTION_STRING
# BD rate computation failed for one of the codecs,
# so to be fair, ignore this source for final results
if source not in black_list_source_various_metrics[metric]:
black_list_source_various_metrics[metric].append(source)
for metric in metrics_for_BDRate:
print('{} black list {} BD RATE\n '.format(sub_sampling, metric.upper()) + repr(black_list_source_various_metrics[metric]))
results = dict()
for codec in codecs:
if codec == 'webp' and sub_sampling == '444':
continue
print('Codec {} subsampling {}, BD rates:'.format(codec, sub_sampling))
result = print_bd_rates(bdrates_various_metrics, codec, unique_sources,
black_list_source_various_metrics,
metrics_for_BDRate)
results[codec] = result
logger.info('\n\n===================' + '=' * 22 * len(metrics_for_BDRate))
results_header = 'Results for subsampling {}'.format(sub_sampling)
logger.info(results_header)
logger.info('-' * len(results_header))
table_header = 'Codec'.ljust(16)
for metric in metrics_for_BDRate:
table_header += ' Mean BDRate-{}'.format(metric.upper()).rjust(22)
logger.info(table_header)
for codec in codecs:
if codec == 'webp' and sub_sampling == '444':
continue
logger.info(results[codec])
logger.info('===================' + '=' * 22 * len(metrics_for_BDRate))
if __name__ == '__main__':
main(sys.argv[1:])