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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(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 = 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])
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])
# 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)
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