def get_full_clips(data_dir, num_clips, num_rec_out=1):
"""
Loads a batch of random clips from the unprocessed train or test data.
@param data_dir: The directory of the data to read. Should be either c.TRAIN_DIR or c.TEST_DIR.
@param num_clips: The number of clips to read.
@param num_rec_out: The number of outputs to predict. Outputs > 1 are computed recursively,
using the previously-generated frames as input. Default = 1.
@return: An array of shape
[num_clips, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + num_rec_out))].
A batch of frame sequences with values normalized in range [-1, 1].
"""
clips = np.empty([num_clips,
c.FULL_HEIGHT,
c.FULL_WIDTH,
(3 * (c.HIST_LEN + num_rec_out))])
# get num_clips random episodes
ep_dirs = np.random.choice(glob(os.path.join(data_dir, '*')), num_clips)
# get a random clip of length HIST_LEN + num_rec_out from each episode
for clip_num, ep_dir in enumerate(ep_dirs):
ep_frame_paths = sorted(glob(os.path.join(ep_dir, '*')))
start_index = np.random.choice(len(ep_frame_paths) - (c.HIST_LEN + num_rec_out - 1))
clip_frame_paths = ep_frame_paths[start_index:start_index + (c.HIST_LEN + num_rec_out)]
# read in frames
for frame_num, frame_path in enumerate(clip_frame_paths):
frame = imread(frame_path, mode='RGB')
norm_frame = normalize_frames(frame)
clips[clip_num, :, :, frame_num * 3:(frame_num + 1) * 3] = norm_frame
return clips
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