forked from RenYang-home/ALVC
-
Notifications
You must be signed in to change notification settings - Fork 0
/
motion.py
171 lines (114 loc) · 6.24 KB
/
motion.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
import tensorflow as tf
import numpy as np
def convnet(im1_warp, im2, flow, layer):
with tf.variable_scope("flow_cnn_" + str(layer), reuse=tf.AUTO_REUSE):
input = tf.concat([im1_warp, im2, flow], axis=-1)
conv1 = tf.layers.conv2d(inputs=input, filters=32, kernel_size=[7, 7], padding="same",
activation=tf.nn.relu)
conv2 = tf.layers.conv2d(inputs=conv1, filters=64, kernel_size=[7, 7], padding="same",
activation=tf.nn.relu)
conv3 = tf.layers.conv2d(inputs=conv2, filters=32, kernel_size=[7, 7], padding="same",
activation=tf.nn.relu)
conv4 = tf.layers.conv2d(inputs=conv3, filters=16, kernel_size=[7, 7], padding="same",
activation=tf.nn.relu)
conv5 = tf.layers.conv2d(inputs=conv4, filters=2 , kernel_size=[7, 7], padding="same",
activation=None)
return conv5
def loss(flow_course, im1, im2, layer):
flow = tf.image.resize_images(flow_course, [tf.shape(im1)[1], tf.shape(im2)[2]])
im1_warped = tf.contrib.image.dense_image_warp(im1, flow)
res = convnet(im1_warped, im2, flow, layer)
flow_fine = res + flow
im1_warped_fine = tf.contrib.image.dense_image_warp(im1, flow_fine)
loss_layer = tf.reduce_mean(tf.squared_difference(im1_warped_fine, im2))
return loss_layer, flow_fine
def optical_flow(im1_4, im2_4, batch, h, w):
im1_3 = tf.layers.average_pooling2d(im1_4, pool_size=2, strides=2, padding='same')
im1_2 = tf.layers.average_pooling2d(im1_3, pool_size=2, strides=2, padding='same')
im1_1 = tf.layers.average_pooling2d(im1_2, pool_size=2, strides=2, padding='same')
im1_0 = tf.layers.average_pooling2d(im1_1, pool_size=2, strides=2, padding='same')
im2_3 = tf.layers.average_pooling2d(im2_4, pool_size=2, strides=2, padding='same')
im2_2 = tf.layers.average_pooling2d(im2_3, pool_size=2, strides=2, padding='same')
im2_1 = tf.layers.average_pooling2d(im2_2, pool_size=2, strides=2, padding='same')
im2_0 = tf.layers.average_pooling2d(im2_1, pool_size=2, strides=2, padding='same')
flow_zero = tf.zeros([batch, h//16, w//16, 2])
loss_0, flow_0 = loss(flow_zero, im1_0, im2_0, 0)
loss_1, flow_1 = loss(flow_0, im1_1, im2_1, 1)
loss_2, flow_2 = loss(flow_1, im1_2, im2_2, 2)
loss_3, flow_3 = loss(flow_2, im1_3, im2_3, 3)
loss_4, flow_4 = loss(flow_3, im1_4, im2_4, 4)
return flow_4, loss_0, loss_1, loss_2, loss_3, loss_4
def tf_inverse_flow(flow_input, b, h, w):
# x = vertical (channel 0 in flow), y = horizontal (channel 1 in flow)
flow_list = tf.unstack(flow_input)
x, y = tf.meshgrid(tf.range(h), tf.range(w), indexing='ij')
x = tf.expand_dims(x, -1)
y = tf.expand_dims(y, -1)
grid = tf.cast(tf.concat([x, y], axis=-1), tf.float32)
for r in range(b):
flow = flow_list[r]
grid1 = grid - flow
x1, y1 = tf.split(grid1, [1, 1], axis=-1)
x1 = tf.clip_by_value(x1, 0, h - 1)
y1 = tf.clip_by_value(y1, 0, w - 1)
grid1 = tf.concat([x1, y1], axis=-1)
grid1 = tf.cast(grid1, tf.int32)
tf_zeros = tf.zeros([h, w, 1, 1], tf.int32)
indices = tf.expand_dims(grid1, 2)
indices = tf.concat([indices, tf_zeros], axis=-1)
flow_x, flow_y = tf.split(flow, [1, 1], axis=-1)
ref_x = tf.Variable(np.zeros([h, w, 1], np.float32), trainable=False, dtype=tf.float32)
ref_y = tf.Variable(np.zeros([h, w, 1], np.float32), trainable=False, dtype=tf.float32)
inv_flow_x = tf.scatter_nd_update(ref_x, indices, -flow_x)
inv_flow_y = tf.scatter_nd_update(ref_y, indices, -flow_y)
inv_flow_batch = tf.expand_dims(tf.concat([inv_flow_x, inv_flow_y], axis=-1), axis=0)
if r == 0:
inv_flow = inv_flow_batch
else:
inv_flow = tf.concat([inv_flow, inv_flow_batch], axis=0)
return inv_flow
def reverse_sample(x_shift, y_shift, h, w, weight):
x, y = tf.meshgrid(tf.range(h), tf.range(w), indexing='ij')
x = tf.cast(tf.expand_dims(x, -1), tf.float32)
y = tf.cast(tf.expand_dims(y, -1), tf.float32)
x -= x_shift
y -= y_shift
x = tf.clip_by_value(x, 0, h - 1)
y = tf.clip_by_value(y, 0, w - 1)
grid1 = tf.concat([x, y], axis=-1)
grid1 = tf.cast(grid1, tf.int32)
tf_zeros = tf.zeros([h, w, 1, 1], tf.int32)
indices = tf.expand_dims(grid1, 2)
indices = tf.concat([indices, tf_zeros], axis=-1)
ref_x = tf.Variable(tf.zeros([h, w, 1], np.float32), trainable=False, dtype=tf.float32)
ref_y = tf.Variable(tf.zeros([h, w, 1], np.float32), trainable=False, dtype=tf.float32)
ref_w = tf.Variable(tf.zeros([h, w, 1], np.float32) + 1e-9, trainable=False, dtype=tf.float32)
inv_flow_x = tf.scatter_nd_update(ref_x, indices, -x_shift * weight)
inv_flow_y = tf.scatter_nd_update(ref_y, indices, -y_shift * weight)
inv_flow_batch = tf.expand_dims(tf.concat([inv_flow_x, inv_flow_y], axis=-1), axis=0)
weight_x = tf.scatter_nd_update(ref_w, indices, weight)
weight_batch = tf.expand_dims(weight_x, axis=0)
return inv_flow_batch, weight_batch
def reverse_flow(flow_input, h, w):
flow_list = tf.unstack(flow_input)
inv_flow = []
for flow in flow_list:
x_flow, y_flow = tf.split(flow, [1, 1], axis=-1)
x_1 = tf.floor(x_flow)
x_2 = x_1 + 1
y_1 = tf.floor(y_flow)
y_2 = y_1 + 1
weight_1 = tf.exp(-((x_flow - x_1) ** 2 + (y_flow - y_1) ** 2))
weight_2 = tf.exp(-((x_flow - x_1) ** 2 + (y_flow - y_2) ** 2))
weight_3 = tf.exp(-((x_flow - x_2) ** 2 + (y_flow - y_1) ** 2))
weight_4 = tf.exp(-((x_flow - x_2) ** 2 + (y_flow - y_2) ** 2))
inv_flow_1, norm_1 = reverse_sample(x_1, y_1, h, w, weight_1)
inv_flow_2, norm_2 = reverse_sample(x_1, y_2, h, w, weight_2)
inv_flow_3, norm_3 = reverse_sample(x_2, y_1, h, w, weight_3)
inv_flow_4, norm_4 = reverse_sample(x_2, y_2, h, w, weight_4)
inv_flow_batch = inv_flow_1 + inv_flow_2 + inv_flow_3 + inv_flow_4
norm_batch = norm_1 + norm_2 + norm_3 + norm_4
inv_flow_norm = tf.divide(inv_flow_batch, norm_batch)
inv_flow.append(inv_flow_norm)
inv_flow = tf.concat(inv_flow, axis=0)
return inv_flow