def populate(self, directory):
'''
Analyzes all audio files in a directory and add their fingerprint
to the database. Writes result to disk when done.
Args:
directory: directory containing audio files.
'''
audio_files = sorted(soundfiles.find_files(directory + '/*'))
c.notice("Reading {} files...".format(len(audio_files)))
i = 0
for filename in audio_files:
i += 1
c.write('\r\t-file ' + str(i) + ' of ' + str(len(audio_files)))
signal = soundfiles.load_signal(filename)
self.add(fingerprint.get_tokens(signal))
c.write("\n")
c.notice("Writing database to disk...")
self.save()
c.succes("Database written")
def populate(self, directory):
'''
Analyzes all audio files in a directory and add their fingerprint
to the database. Writes result to disk when done.
Args:
directory: directory containing audio files.
'''
audio_files = sorted(soundfiles.find_files(directory + '/*'))
c.notice("Reading {} files...".format(len(audio_files)))
i = 0
for filename in audio_files:
i += 1
c.write('\r\t-file ' + str(i) + ' of ' + str(len(audio_files)))
signal = soundfiles.load_signal(filename)
self.add(fingerprint.get_tokens(signal))
c.write("\n")
c.notice("Writing database to disk...")
self.save()
c.succes("Database written")
if args.train:
database.populate(args.train)
if args.f:
if not database.read:
c.notice('Loading database from disk...')
database.load()
c.succes('Loaded {} entries.'.format(database.get_size()))
c.notice('Converting database to classifier...')
classifier = database.as_classifier()
c.notice('Loading \'{}\' from disk...'.format(args.f))
signal = load_signal(args.f)
if signal is None:
c.fatal('This file could not be loaded')
parser.print_help()
exit()
c.notice('Analyzing \'{}\'...'.format(signal.get_filename()))
tokens = get_tokens(signal)
c.notice("Classifying...")
match = classifier.classify(tokens)
if match:
c.notice("File \'{}\' matches with database entry {}".format(
signal.filename, match))
else:
c.fatal("File \'{}\' could not be matched".format(signal.filename))
if args.f:
if not database.read:
c.notice('Loading database from disk...')
database.load()
c.succes('Loaded {} entries.'.format(database.get_size()))
c.notice('Converting database to classifier...')
classifier = database.as_classifier()
c.notice('Loading \'{}\' from disk...'.format(args.f))
signal = load_signal(args.f)
if signal is None:
c.fatal('This file could not be loaded')
parser.print_help()
exit()
c.notice('Analyzing \'{}\'...'.format(signal.get_filename()))
tokens = get_tokens(signal)
c.notice("Classifying...")
match = classifier.classify(tokens)
if match:
c.notice("File \'{}\' matches with database entry {}".format(
signal.filename, match
))
else:
c.fatal(
"File \'{}\' could not be matched".format(signal.filename)
)