(data_dir, num_clips, num_rec_out=1)
return clips
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].
Loads a batch of random clips from the unprocessed train or test data.
Loads a batch of random clips from the unprocessed train or test data .
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
def get_full_clips ( data_dir , num_clips , num_rec_out = 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