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import tensorflow as tf
import numpy as np
from collections import OrderedDict
import os
import glob
import cv2
rng = np.random.RandomState(2017)
def np_load_frame(filename, resize_height, resize_width):
image_decoded = cv2.imread(filename)
image_resized = cv2.resize(image_decoded, (resize_width, resize_height))
image_resized = image_resized.astype(dtype=np.float32)
image_resized = (image_resized / 127.5) - 1.0
return image_resized
class DataLoader(object):
def __init__(self, video_folder, resize_height=256, resize_width=256):
self.dir = video_folder
self.videos = {}
self._resize_height = resize_height
self._resize_width = resize_width
self.setup()
def __call__(self, batch_size, time_steps, num_pred=1):
video_info_list = list(self.videos.values())
num_videos = len(video_info_list)
clip_length = time_steps + num_pred
resize_height, resize_width = self._resize_height, self._resize_width
def video_clip_generator():
v_id = -1
while True:
v_id = (v_id + 1) % num_videos
video_info = video_info_list[v_id]
start = rng.randint(0, video_info['length'] - clip_length)
video_clip = []
for frame_id in range(start, start + clip_length):
video_clip.append(np_load_frame(video_info['frame'][frame_id], resize_height, resize_width))
video_clip = np.concatenate(video_clip, axis=2)
yield video_clip
# video clip paths
dataset = tf.data.Dataset.from_generator(generator=video_clip_generator,
output_types=tf.float32,
output_shapes=[resize_height, resize_width, clip_length * 3])
print('generator dataset, {}'.format(dataset))
dataset = dataset.prefetch(buffer_size=1000)
dataset = dataset.shuffle(buffer_size=1000).batch(batch_size)
print('epoch dataset, {}'.format(dataset))
return dataset
def __getitem__(self, video_name):
assert video_name in self.videos.keys(), 'video = {} is not in {}!'.format(video_name, self.videos.keys())
return self.videos[video_name]
def setup(self):
videos = glob.glob(os.path.join(self.dir, '*'))
for video in sorted(videos):
video_name = video.split('/')[-1]
self.videos[video_name] = {}
self.videos[video_name]['path'] = video
self.videos[video_name]['frame'] = glob.glob(os.path.join(video, '*.jpg'))
self.videos[video_name]['frame'].sort()
self.videos[video_name]['length'] = len(self.videos[video_name]['frame'])
def get_video_clips(self, video, start, end):
# assert video in self.videos, 'video = {} must in {}!'.format(video, self.videos.keys())
# assert start >= 0, 'start = {} must >=0!'.format(start)
# assert end <= self.videos[video]['length'], 'end = {} must <= {}'.format(video, self.videos[video]['length'])
batch = []
for i in range(start, end):
image = np_load_frame(self.videos[video]['frame'][i], self._resize_height, self._resize_width)
batch.append(image)
return np.concatenate(batch, axis=2)
# def get_video_clips(self, video_name, start, end):
# video_idx = np.arange(start, end)
# video_clip = np.empty(shape=[self._resize_height, self._resize_height, 3*len(video_idx)], dtype=np.float32)
# for idx, v_idx in enumerate(video_idx):
# filename = self.videos[video_name]['frame'][v_idx]
# video_clip[..., idx*3:(idx+1)*3] = np_load_frame(filename, self._resize_height, self._resize_width)
#
# return video_clip
def log10(t):
"""
Calculates the base-10 log of each element in t.
@param t: The tensor from which to calculate the base-10 log.
@return: A tensor with the base-10 log of each element in t.
"""
numerator = tf.log(t)
denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
return numerator / denominator
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])
gt_frames = (gt_frames + 1.0) / 2.0
gen_frames = (gen_frames + 1.0) / 2.0
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, channels=3):
"""
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.
@param channels: The number of channels, 3 is RGB and 1 is Gray, default is 3.
@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(channels), dtype=tf.float32)
neg = -1 * pos
filter_x = tf.expand_dims(tf.stack([neg, pos]), 0) # [-1, 1]
filter_y = tf.stack([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)
def diff_mask(gen_frames, gt_frames, min_value=-1, max_value=1):
# normalize to [0, 1]
delta = max_value - min_value
gen_frames = (gen_frames - min_value) / delta
gt_frames = (gt_frames - min_value) / delta
gen_gray_frames = tf.image.rgb_to_grayscale(gen_frames)
gt_gray_frames = tf.image.rgb_to_grayscale(gt_frames)
diff = tf.abs(gen_gray_frames - gt_gray_frames)
return diff
def load(saver, sess, ckpt_path):
saver.restore(sess, ckpt_path)
print("Restored model parameters from {}".format(ckpt_path))
def save(saver, sess, logdir, step):
model_name = 'model.ckpt'
checkpoint_path = os.path.join(logdir, model_name)
if not os.path.exists(logdir):
os.makedirs(logdir)
saver.save(sess, checkpoint_path, global_step=step)
print('The checkpoint has been created.')
# if __name__ == '__main__':
# os.environ['CUDA_DEVICES_ORDER'] = "PCI_BUS_ID"
# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#
# data_loader = DataLoader('/home/liuwen/ssd/datasets/avenue/training/frames')
# dataset, epoch_size = data_loader(10, 4, 1, 3, 1)
#
# # debug
# iteration = dataset.make_one_shot_iterator()
# batch_video_clip_tensor = iteration.get_next()
#
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# with tf.Session(config=config) as sess:
# # batch_video_clip = sess.run(next(it))
#
# for i in range(100):
# batch_video_clip = sess.run(batch_video_clip_tensor)
# # print(batch_video_clip.shape)
#
# for vid, video_clip in enumerate(batch_video_clip):
# for fid, frame in enumerate(video_clip):
# print(i, vid, fid)
# cv2.imshow('visualization', frame + 0.5)
# cv2.waitKey(100)
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