# Loop through data.
pbar = tqdm(total=len(data.data))
for video in data.data:
# Get the path to the sequence for this video.
path = os.path.join('data', 'sequences', video[1]+video[2] + '-' + str(seq_length) + \
'-features') # numpy will auto-append .npy
# Check if we already have it.
if os.path.isfile(path + '.npy'):
pbar.update(1)
continue
# Get the frames for this video.
# frames = data.get_frames_for_sample(video)
frames = data.get_features_for_sample(video)
print(video[2])
# Now downsample to just the ones we need.
frames = data.rescale_list(frames, seq_length)
# frames1 = data.get_frames_for_sample(video)
# frames1 = data.rescale_list(frames1, seq_length)
frames1 = [w.replace('txt', 'jpg') for w in frames]
# Now loop through and extract features to build the sequence.
sequence = []
for (i,j) in itertools.izip(frames,frames1):
features=[]
ifeatures=[]
cfeatures=[]
# print(i)
