def uploadFile(self, bucket, folder, local_path, chunk_size=52428800):
"""
Upload local_filename to bucket/folder/local_filename in bucket
Same filename gets overwritten
"""
source_size = os.stat(local_path).st_size
chunk_count = int(math.ceil(source_size / float(chunk_size)))
# local_filename = local_path.split('/')[-1]
try:
bucket = self.connection.get_bucket(bucket) # select bucket
except Exception as e:
print('Problem getting bucket %s' % e)
return
if bucket:
# create a multipart upload request
mp = bucket.initiate_multipart_upload(os.path.basename(local_path))
"""
try:
key = bucket.new_key(os.path.join(folder, local_filename))
except Exception as e:
print('Folder %s not accessible %s'%(folder,e))
key.set_contents_from_filename(local_path)
"""
done = 0
for i in range(chunk_count):
offset = chunk_size * i
bytes = min(chunk_size, source_size - offset)
with FileChunkIO(local_path, 'r', offset=offset,
bytes=bytes) as fp:
mp.upload_part_from_file(fp, part_num=i + 1)
done += chunk_size
if source_size > done:
ut.printStuff('Uploading file %s %%',
(int(100. * done / source_size - 1)))
mp.complete_upload()
print('File uploaded correctly')
print
else:
print('%s does not exist' % bucket)
import utils as ut
# Load sound files
path = 'data/'
sound_file_paths = [os.path.join(path, "helpme.wav"),
os.path.join(path, "podcast_17_sample.wav"),
]
sound_names = ["helpme", "podcast_17_sample"]
raw_sounds = ut.load_sound_files(sound_file_paths)
windowsize = 6000 # size of sliding window (22050 samples == 0.5 sec)
step = 3000
maxfiles = 10000
numfiles = 0
dimx = 6
dimy = 5
# create positive samples
audiosamples = raw_sounds[0]
numsamples = audiosamples.shape[0]
for x in range(0, numsamples-windowsize, step):
numfiles +=1
b = x # begin
e = b+windowsize # end
ut.printStuff('Creating spectrum image class_1 samples [%d-%d] of %d file %d',(b,e, numsamples, numfiles))
filename = os.path.join(path, 'class_1/partial_spectrum_%d.png'%x)
ut.specgram_frombuffer(audiosamples[b:e], dimx, dimy, fname=filename, dpi=180)
print('\nbye!\n')
image_path = path.join(config.data_dir, "images")
if not path.isdir(image_path):
mkdir(image_path)
windowsize = 6000 # size of sliding window (22050 samples == 0.5 sec)
step = 3000
numfiles = 0
dimx = 6
dimy = 5
for i in range(len(raw_sounds)):
# create samples
numsamples = raw_sounds[i].shape[0]
file_path = path.basename(sound_files[i])
file_path = path.splitext(file_path)[0]
for x in range(0, numsamples - windowsize, step):
b = x # begin
e = x + windowsize # end
fmt_string = "(%d/%d) %s [%d-%d] of %d file %d"
ut.printStuff(fmt_string, (i, len(raw_sounds) - 1, file_path, x, e, numsamples, numfiles))
filename = path.join(image_path, "{}_{}.png".format(file_path, x))
ut.specgram_frombuffer(raw_sounds[i][x:e], dimx, dimy, fname=filename, dpi=180)
numfiles += 1
print('\nbye!\n')
# Load sound files
path = '/archive/ahem_data/'
sound_file_paths = [os.path.join(path, "ahem_sounds.wav"),
os.path.join(path, "podcast_17_sample.wav"),
]
sound_names = ["ahem_sounds", "podcast_17_sample"]
raw_sounds = ut.load_sound_files(sound_file_paths)
windowsize = 6000 # size of sliding window (22050 samples == 0.5 sec)
step = 3000
maxfiles = 10000
numfiles = 0
dimx = 6
dimy = 5
# create negative samples
audiosamples = raw_sounds[1]
numsamples = audiosamples.shape[0]
for x in xrange(0, numsamples-windowsize, step):
numfiles += 1
b = x # begin
e = b+windowsize # end
ut.printStuff('Creating spectrum image class_0 samples [%d-%d] of %d file %d',(b,e, numsamples, numfiles))
filename = os.path.join(path, 'class_0/partial_spectrum_%d.png'%x)
ut.specgram_frombuffer(audiosamples[b:e], dimx, dimy, fname=filename, dpi=180)
print('\nbye!\n')