Y0_com_img = misc.imread(args.ref)
Y1_raw_img = misc.imread(args.raw)
Y0_com_img = np.expand_dims(Y0_com_img, 0)
Y1_raw_img = np.expand_dims(Y1_raw_img, 0)
Height = np.size(Y1_raw_img, 1)
Width = np.size(Y1_raw_img, 2)
Y0_com = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
Y1_raw = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
with tf.variable_scope("flow_motion"):
flow_tensor, _, _, _, _, _ = motion.optical_flow(Y0_com, Y1_raw, batch_size, Height, Width)
# Y1_warp_0 = tf.contrib.image.dense_image_warp(Y0_com, flow_tensor)
# Encode flow
flow_latent = CNN_img.MV_analysis(flow_tensor, args.N, args.M)
entropy_bottleneck_mv = tfc.EntropyBottleneck()
string_mv = entropy_bottleneck_mv.compress(flow_latent)
string_mv = tf.squeeze(string_mv, axis=0)
flow_latent_hat, MV_likelihoods = entropy_bottleneck_mv(flow_latent, training=False)
flow_hat = CNN_img.MV_synthesis(flow_latent_hat, args.N)
# Motion Compensation
Y1_warp = tf.contrib.image.dense_image_warp(Y0_com, flow_hat)
Y2_raw_img = misc.imread(args.raw_2)
Y0_com_img = np.expand_dims(Y0_com_img, 0)
Y1_raw_img = np.expand_dims(Y1_raw_img, 0)
Y2_raw_img = np.expand_dims(Y2_raw_img, 0)
Height = np.size(Y1_raw_img, 1)
Width = np.size(Y1_raw_img, 2)
Y0_com = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
Y1_raw = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
Y2_raw = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
with tf.variable_scope("flow_motion", reuse=False):
flow_20, _, _, _, _, _ = motion.optical_flow(Y0_com, Y2_raw, batch_size,
Height, Width)
# Y2_warp_0 = tf.contrib.image.dense_image_warp(Y0_com_tensor, flow_20)
with tf.variable_scope("motion_compression", reuse=False):
flow_latent = CNN_img.MV_analysis(flow_20, num_filters=args.N, M=args.M)
entropy_mv = tfc.EntropyBottleneck()
string_mv = entropy_mv.compress(flow_latent)
string_mv = tf.squeeze(string_mv, axis=0)
flow_latent_hat, MV_likelihoods = entropy_mv(flow_latent, training=False)
flow_20_hat = CNN_img.MV_synthesis(flow_latent_hat, num_filters=args.N)
with tf.variable_scope("motion_estimation", reuse=False):
