import tensorflow as tf import numpy as np from scipy.ndimage import imread from glob import glob import os import constants as c from tfutils import log10 ## # Data ## def normalize_frames(frames): """ Convert frames from int8 [0, 255] to float32 [-1, 1]. @param frames: A numpy array. The frames to be converted. @return: The normalized frames. """ new_frames = frames.astype(np.float32) new_frames /= (255 / 2) new_frames -= 1 return new_frames def denormalize_frames(frames): """ Performs the inverse operation of normalize_frames. @param frames: A numpy array. The frames to be converted. @return: The denormalized frames. """ new_frames = frames + 1 new_frames *= (255 / 2) # noinspection PyUnresolvedReferences new_frames = new_frames.astype(np.uint8) return new_frames def clip_l2_diff(clip): """ @param clip: A numpy array of shape [c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))]. @return: The sum of l2 differences between the frame pixels of each sequential pair of frames. """ diff = 0 for i in xrange(c.HIST_LEN): frame = clip[:, :, 3 * i:3 * (i + 1)] next_frame = clip[:, :, 3 * (i + 1):3 * (i + 2)] # noinspection PyTypeChecker diff += np.sum(np.square(next_frame - frame)) return diff 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 process_clip(): """ Gets a clip from the train dataset, cropped randomly to c.TRAIN_HEIGHT x c.TRAIN_WIDTH. @return: An array of shape [c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))]. A frame sequence with values normalized in range [-1, 1]. """ clip = get_full_clips(c.TRAIN_DIR, 1)[0] # Randomly crop the clip. With 0.05 probability, take the first crop offered, otherwise, # repeat until we have a clip with movement in it. take_first = np.random.choice(2, p=[0.95, 0.05]) cropped_clip = np.empty([c.TRAIN_HEIGHT, c.TRAIN_WIDTH, 3 * (c.HIST_LEN + 1)]) for i in xrange(100): # cap at 100 trials in case the clip has no movement anywhere crop_x = np.random.choice(c.FULL_WIDTH - c.TRAIN_WIDTH + 1) crop_y = np.random.choice(c.FULL_HEIGHT - c.TRAIN_HEIGHT + 1) cropped_clip = clip[crop_y:crop_y + c.TRAIN_HEIGHT, crop_x:crop_x + c.TRAIN_WIDTH, :] if take_first or clip_l2_diff(cropped_clip) > c.MOVEMENT_THRESHOLD: break return cropped_clip def get_train_batch(): """ Loads c.BATCH_SIZE clips from the database of preprocessed training clips. @return: An array of shape [c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))]. """ clips = np.empty([c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))], dtype=np.float32) for i in xrange(c.BATCH_SIZE): path = c.TRAIN_DIR_CLIPS + str(np.random.choice(c.NUM_CLIPS)) + '.npz' clip = np.load(path)['arr_0'] clips[i] = clip return clips def get_test_batch(test_batch_size, num_rec_out=1): """ Gets a clip from the test dataset. @param test_batch_size: The number of clips. @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: [test_batch_size, c.TEST_HEIGHT, c.TEST_WIDTH, (3 * (c.HIST_LEN + num_rec_out))]. A batch of frame sequences with values normalized in range [-1, 1]. """ return get_full_clips(c.TEST_DIR, test_batch_size, num_rec_out=num_rec_out) ## # Error calculation ## # TODO: Add SSIM error http://www.cns.nyu.edu/pub/eero/wang03-reprint.pdf # TODO: Unit test error functions. def psnr_error(gen_frames, gt_frames): """ Computes the Peak Signal to Noise Ratio error between the generated images and the ground truth images. @param gen_frames: A tensor of shape [batch_size, height, width, 3]. The frames generated by the generator model. @param gt_frames: A tensor of shape [batch_size, height, width, 3]. The ground-truth frames for each frame in gen_frames. @return: A scalar tensor. The mean Peak Signal to Noise Ratio error over each frame in the batch. """ shape = tf.shape(gen_frames) num_pixels = tf.to_float(shape[1] * shape[2] * shape[3]) square_diff = tf.square(gt_frames - gen_frames) batch_errors = 10 * log10(1 / ((1 / num_pixels) * tf.reduce_sum(square_diff, [1, 2, 3]))) return tf.reduce_mean(batch_errors) def sharp_diff_error(gen_frames, gt_frames): """ Computes the Sharpness Difference error between the generated images and the ground truth images. @param gen_frames: A tensor of shape [batch_size, height, width, 3]. The frames generated by the generator model. @param gt_frames: A tensor of shape [batch_size, height, width, 3]. The ground-truth frames for each frame in gen_frames. @return: A scalar tensor. The Sharpness Difference error over each frame in the batch. """ shape = tf.shape(gen_frames) num_pixels = tf.to_float(shape[1] * shape[2] * shape[3]) # gradient difference # create filters [-1, 1] and [[1],[-1]] for diffing to the left and down respectively. # TODO: Could this be simplified with one filter [[-1, 2], [0, -1]]? pos = tf.constant(np.identity(3), dtype=tf.float32) neg = -1 * pos filter_x = tf.expand_dims(tf.pack([neg, pos]), 0) # [-1, 1] filter_y = tf.pack([tf.expand_dims(pos, 0), tf.expand_dims(neg, 0)]) # [[1],[-1]] strides = [1, 1, 1, 1] # stride of (1, 1) padding = 'SAME' gen_dx = tf.abs(tf.nn.conv2d(gen_frames, filter_x, strides, padding=padding)) gen_dy = tf.abs(tf.nn.conv2d(gen_frames, filter_y, strides, padding=padding)) gt_dx = tf.abs(tf.nn.conv2d(gt_frames, filter_x, strides, padding=padding)) gt_dy = tf.abs(tf.nn.conv2d(gt_frames, filter_y, strides, padding=padding)) gen_grad_sum = gen_dx + gen_dy gt_grad_sum = gt_dx + gt_dy grad_diff = tf.abs(gt_grad_sum - gen_grad_sum) batch_errors = 10 * log10(1 / ((1 / num_pixels) * tf.reduce_sum(grad_diff, [1, 2, 3]))) return tf.reduce_mean(batch_errors)