def worker(id):
print('Worker', id, 'started.')
while not taskQueue.empty():
clip_num = taskQueue.get()
if clip_num is None:
break
clip = process_clip()
np.savez_compressed(c.TRAIN_DIR_CLIPS + str(clip_num), clip)
if (clip_num + 1) % 100 == 0:
print('Worker %d: Processed %d clips' % (id, clip_num + 1))
print('Worker', id, 'finished.')
def process_training_data(num_clips):
"""
Processes random training clips from the full training data. Saves to TRAIN_DIR_CLIPS by
default.
:param num_clips: The number of clips to process. Default = 5000000
:warning: This can take a couple of hours to complete with large numbers of clips.
"""
num_prev_clips = len(glob(c.TRAIN_DIR_CLIPS + '*'))
for clip_num in range(num_prev_clips, num_clips + num_prev_clips):
clip = process_clip()
np.savez_compressed(c.TRAIN_DIR_CLIPS + str(clip_num), clip)
if (clip_num + 1) % 100 == 0: print ('Processed %d clips' % (clip_num + 1))
def process_training_data(num_clips):
num_prev_clips = len(glob(c.TRAIN_DIR_CLIPS + '*'))
for clip_num in xrange(num_prev_clips, num_clips + num_prev_clips):
clip = process_clip()
np.savez_compressed(c.TRAIN_DIR_CLIPS + str(clip_num), clip)
if (clip_num + 1) % 100 == 0: print 'Processed %d clips' % (clip_num + 1)
