def run():
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter)
parser.add_argument('-s', '--seconds', nargs='?')
args = parser.parse_args()
if not args.seconds:
# parser.print_help()
# sys.exit(0)
seconds = int(5)
args.seconds = seconds
# seconds = int(args.seconds)
chunksize = 2**12 # 4096
channels = 2#int(config['channels']) # 1=mono, 2=stereo
record_forever = False
visualise_console = bool(config['mic.visualise_console'])
visualise_plot = bool(config['mic.visualise_plot'])
reader = MicrophoneReader(None)
reader.start_recording(seconds=seconds,
chunksize=chunksize,
channels=channels)
msg = ' * started recording..'
print(colored(msg, attrs=['dark']))
while True:
bufferSize = int(reader.rate / reader.chunksize * seconds)
for i in range(0, bufferSize):
nums = reader.process_recording()
if visualise_console:
msg = colored('%05d', attrs=['dark']) + colored(' %s', 'green')
print(msg % visual_peak.calc(nums))
else:
msg = 'processing %d of %d..' % (i, bufferSize)
print(colored(msg, attrs=['dark']))
if not record_forever: break
if visualise_plot:
data = reader.get_recorded_data()[0]
visual_plot.show(data)
reader.stop_recording()
msg = ' * recording has been stopped'
print(colored(msg, attrs=['dark']))
def grouper(iterable, n, fillvalue=None):
args = [iter(iterable)] * n
liste = [list(filter(None, values)) for values in zip_longest(fillvalue=fillvalue, *args)]
# print('liste :', liste)
return liste
data = reader.get_recorded_data()
msg = ' * recorded %d samples'
print(colored(msg, attrs=['dark']) % len(data[0]))
# reader.save_recorded('test.wav')
Fs = fingerprint.DEFAULT_FS
channel_amount = len(data)
result = set()
matches = []
# print("matches :", matches)
def find_matches(samples, Fs=fingerprint.DEFAULT_FS):
hashes = fingerprint.fingerprint(samples, Fs=Fs)
return return_matches(hashes)
def return_matches(hashes):
mapper = {}
for hash, offset in hashes:
mapper[hash.upper()] = offset
values = mapper.keys()
for split_values in grouper(values, 1000):
# @todo move to db related files
query = """
SELECT upper(hash), song_fk, offset
FROM fingerprints
WHERE upper(hash) IN (%s)
"""
query = query % ','.join('?' * len(split_values))
# print('query:', query)
# print('split_values', split_values)
x = db.executeAll(query, split_values)
# print('X', x)
matches_found = len(x)
if matches_found > 0:
msg = '** found %d hash matches (step %d/%d)'
print(colored(msg, 'green') % (
matches_found,
len(split_values),
len(values)
))
else:
msg = '** not matches found (step %d/%d)'
print(colored(msg, 'red') % (
len(split_values),
len(values)
))
for hash, sid, offset in x:
# (sid, db_offset - song_sampled_offset)
# print('hash', hash)
# print('sid', sid)
# print('offset', offset)
# print(' mapper[hash]', mapper[hash])
# yield (sid, offset - mapper[hash])
yield (sid, mapper[hash])
for channeln, channel in enumerate(data):
# TODO: Remove prints or change them into optional logging.
msg = 'fingerprinting channel %d/%d'
print(colored(msg, attrs=['dark']) % (channeln+1, channel_amount))
matches.extend(find_matches(channel))
msg = 'finished channel %d/%d, got %d hashes'
print(colored(msg, attrs=['dark']) % (
channeln+1, channel_amount, len(matches)
))
def align_matches(matches):
diff_counter = {}
largest = 0
largest_count = 0
song_id = -1
for tup in matches:
sid, diff = tup
if diff not in diff_counter:
diff_counter[diff] = {}
if sid not in diff_counter[diff]:
diff_counter[diff][sid] = 0
diff_counter[diff][sid] += 1
if diff_counter[diff][sid] > largest_count:
largest = diff
largest_count = diff_counter[diff][sid]
song_id = sid
songM = db.get_song_by_id(song_id)
nseconds = round(float(largest) / fingerprint.DEFAULT_FS *
fingerprint.DEFAULT_WINDOW_SIZE *
fingerprint.DEFAULT_OVERLAP_RATIO, 5)
return {
"SONG_ID" : song_id,
"SONG_NAME" : songM[1],
"CONFIDENCE" : largest_count,
"OFFSET" : int(largest),
"OFFSET_SECS" : nseconds
}
total_matches_found = len(matches)
print('')
if total_matches_found > 0:
msg = ' ** totally found %d hash matches'
print(colored(msg, 'green') % total_matches_found)
song = align_matches(matches)
#print(song['SONG_NAME'])
msg = ' => song: %s (id=%d)\n'
msg += 'offset: %d (%d secs)\n'
msg += 'confidence: %d'
print(colored(msg, 'green') % (
song['SONG_NAME'], song['SONG_ID'],
song['OFFSET'], song['OFFSET_SECS'],
song['CONFIDENCE']
))
# Regex pour virer le .MP3
pattern = re.compile(r"[A-Z][a-z]*")
result = pattern.search(song['SONG_NAME'])
print(result.group())
return song
else:
msg = {'error':'Not matches found at all'}
print(colored(msg['error'], 'red'))
return msg
#if __name__ == '__main__':
# run()
nums = reader.process_recording()
if visualise_console:
msg = colored(' %05d', attrs=['dark']) + colored(' %s', 'green')
print msg % visual_peak.calc(nums)
else:
msg = ' processing %d of %d..' % (i, bufferSize)
print colored(msg, attrs=['dark'])
if not record_forever: break
if visualise_plot:
data = reader.get_recorded_data()[0]
visual_plot.show(data)
reader.stop_recording()
msg = ' * recording has been stopped'
print colored(msg, attrs=['dark'])
def grouper(iterable, n, fillvalue=None):
args = [iter(iterable)] * n
return (filter(None, values) for values
in izip_longest(fillvalue=fillvalue, *args))
data = reader.get_recorded_data()
msg = ' * recorded %d samples'
print colored(msg, attrs=['dark']) % len(data[0])
def run_recognition():
config = get_config()
# Set up logging
handlers = []
if bool(config["log.console_out"]):
handlers.append(logging.StreamHandler())
if bool(config["log.file_out"]):
handlers.append(logging.FileHandler("microphone_rec.log"))
logger = logging.basicConfig(
handlers=handlers,
format=config["log.format"],
level=config["log.level"],
)
db = SqliteDatabase()
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter)
parser.add_argument("-s", "--seconds", nargs="?")
args = parser.parse_args()
if not args.seconds:
parser.print_help()
sys.exit(0)
seconds = int(args.seconds)
chunksize = 2**12 # 4096
channels = int(config["channels"]) # 1=mono, 2=stereo
record_forever = False
save_recorded = bool(config["mic.save_recorded"])
visualise_console = bool(config["mic.visualise_console"])
visualise_plot = bool(config["mic.visualise_plot"])
reader = MicrophoneReader()
reader.start_recording(seconds=seconds,
chunksize=chunksize,
channels=channels)
msg = " * started recording.."
logger.info(msg)
# print(colored(msg, attrs=["dark"]))
while True:
bufferSize = int(reader.rate / reader.chunksize * seconds)
for i in range(0, bufferSize):
nums = reader.process_recording()
if visualise_console:
msg = colored(" %05d", attrs=["dark"]) + colored(
" %s", "green")
logger.info(msg, visual_peak.calc(nums))
# print(msg % visual_peak.calc(nums))
else:
msg = " processing %d of %d.." % (i, bufferSize)
logger.info(msg)
# print(colored(msg, attrs=["dark"]))
if not record_forever:
break
if visualise_plot:
data = reader.get_recorded_data()[0]
visual_plot.show(data)
reader.stop_recording()
msg = " * recording has been stopped"
logger.info(msg)
# print(colored(msg, attrs=["dark"]))
data = reader.get_recorded_data()
msg = " * recorded %d samples"
logger.info(msg, len(data[0]))
# print(colored(msg, attrs=["dark"]) % len(data[0]))
if save_recorded:
reader.save_recorded("test.wav")
Fs = fingerprint.DEFAULT_FS
channel_amount = len(data)
matches = []
for channeln, channel in enumerate(data):
msg = " fingerprinting channel %d/%d"
logger.info(msg, channeln + 1, channel_amount)
# print(colored(msg, attrs=["dark"]) % (channeln + 1, channel_amount))
matches.extend(find_matches(db, channel, logger, Fs))
msg = " finished channel %d/%d, got %d hashes"
logger.info(msg, channeln + 1, channel_amount, len(matches))
# print(
# colored(msg, attrs=["dark"])
# % (channeln + 1, channel_amount, len(matches))
# )
print_match_results(db, matches, logger)