update

1 files changed, 10 insertions, 76 deletions

diff --git a/Codes/utils.py b/Codes/utils.py
index efeab8e..8466d08 100644
--- a/Codes/utils.py
+++ b/Codes/utils.py
@@ -10,6 +10,15 @@ rng = np.random.RandomState(2017)
 
 
 def np_load_frame(filename, resize_height, resize_width):
+    """
+    Load image path and convert it to numpy.ndarray. Notes that the color channels are BGR and the color space
+    is normalized from [0, 255] to [-1, 1].
+
+    :param filename: the full path of image
+    :param resize_height: resized height
+    :param resize_width: resized width
+    :return: numpy.ndarray
+    """
     image_decoded = cv2.imread(filename)
     image_resized = cv2.resize(image_decoded, (resize_width, resize_height))
     image_resized = image_resized.astype(dtype=np.float32)
@@ -20,7 +29,7 @@ def np_load_frame(filename, resize_height, resize_width):
 class DataLoader(object):
     def __init__(self, video_folder, resize_height=256, resize_width=256):
         self.dir = video_folder
-        self.videos = {}
+        self.videos = OrderedDict()
         self._resize_height = resize_height
         self._resize_width = resize_width
         self.setup()
@@ -83,15 +92,6 @@ class DataLoader(object):
 
         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):
     """
@@ -130,46 +130,6 @@ def psnr_error(gen_frames, gt_frames):
     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
@@ -197,31 +157,5 @@ def save(saver, sess, logdir, 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)
-