1 from __future__ import division
2 import matplotlib.pyplot as plt
3 import numpy as np
4 import tensorflow as tf
5
6
7 imgs = [[[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
8 [[255, 0, 0], [0, 255, 0], [0, 0, 255]],
9 [[255, 0, 0], [0, 255, 0], [0, 0, 255]],
10 [[255, 0, 0], [0, 255, 0], [0, 0, 255]]],
11 [[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
12 [[255, 0, 0], [0, 255, 0], [0, 0, 255]],
13 [[255, 0, 0], [0, 255, 0], [0, 0, 255]],
14 [[255, 0, 0], [0, 255, 0], [0, 0, 255]]]]
15 imgs = tf.reshape(imgs, [2, 4, 3, 3])
16 coords = [[[[0.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
17 [[0.5, 2], [1.3, 2], [0.2, 1.6]],
18 [[0.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
19 [[0.5, 2], [1.3, 2], [0.2, 1.6]]],
20 [[[0.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
21 [[0.5, 2], [1.3, 2], [0.2, 1.6]],
22 [[0.2, 0.2], [1.3, 0.2], [1.8, 2.2]],
23 [[0.5, 2], [1.3, 2], [0.2, 1.6]]]]
24 coords = tf.reshape(coords, [2, 4, 3, 2])
25
26
27 cam_coords = [[[[1, 2, 3, 1], [2, 3, 4, 1], [3, 4, 5, 1]],
28 [[4, 5, 6, 1], [7, 8, 9, 1], [10, 11, 12, 1]],
29 [[1, 0, 3, 1], [3, 3, 4, 1], [3, 5, 5, 1]],
30 [[4, 4, 6, 1], [7, 7, 9, 1], [10, 12, 12, 1]]],
31 [[[1, 2, 3, 1], [2, 3, 4, 1], [3, 4, 5, 1]],
32 [[4, 5, 6, 1], [7, 8, 9, 1], [10, 11, 12, 1]],
33 [[1, 0, 3, 1], [3, 3, 4, 1], [3, 5, 5, 1]],
34 [[4, 4, 6, 1], [7, 7, 9, 1], [10, 12, 12, 1]]]]
35 cam_coords = tf.reshape(cam_coords, [2, 4, 3, 4])
36
37
38 def bilinear_sampler(imgs, coords, cam_coords):
39 def _repeat(x, n_repeats): # x = tf.cast(tf.range(4), 'float32') * 53248 n_repeats = 53248。
40 rep = tf.transpose(
41 tf.expand_dims(tf.ones(shape=tf.stack([
42 n_repeats,
43 ])), 1), [1,
44 0]) # 最后得到[1,53248]大小的全一矩阵。tf.stack其作用类似于tf.concat，都是拼接两个张量，而不同之处在于，tf.concat拼接的是两个shape完全相同的张量，并且产生的张量的阶数不会发生变化，而tf.stack则会在新的张量阶上拼接，产生的张量的阶数将会增加
45 rep = tf.cast(rep, 'float32')
46 x = tf.matmul(tf.reshape(x, (-1, 1)),
47 rep) # reshape为一列，得到[[ 0.][ 53248.][106496.][159744.]]*rep，最后得到shape=(4, 53248)的矩阵。
48 return tf.reshape(x, [-1]) # 最后又化为一列Tensor("Reshape_1:0", shape=(212992,), dtype=float32)
49
50 with tf.name_scope('image_sampling'):
51 coords_x, coords_y = tf.split(coords, [1, 1], axis=3)
52 inp_size = imgs.get_shape()
53 coord_size = coords.get_shape()
54 out_size = coords.get_shape().as_list()
55 out_size[3] = imgs.get_shape().as_list()[3]
56
57 coords_x = tf.cast(coords_x, 'float32')
58 coords_y = tf.cast(coords_y, 'float32')
59
60 x0 = tf.floor(coords_x)
61 x1 = x0 + 1
62 y0 = tf.floor(coords_y)
63 y1 = y0 + 1
64
65 y_max = tf.cast(tf.shape(imgs)[1] - 1, 'float32')
66 x_max = tf.cast(tf.shape(imgs)[2] - 1, 'float32')
67 zero = tf.zeros([1], dtype='float32')
68
69 x0_safe = tf.clip_by_value(x0, zero, x_max)
70 y0_safe = tf.clip_by_value(y0, zero, y_max)
71 x1_safe = tf.clip_by_value(x1, zero, x_max)
72 y1_safe = tf.clip_by_value(y1, zero, y_max)
73
74 ## bilinear interp weights, with points outside the grid having weight 0#判断是否相等，相等为1，不相等为0.
75 # wt_x0 = (x1 - coords_x) * tf.cast(tf.equal(x0, x0_safe), 'float32')
76 # wt_x1 = (coords_x - x0) * tf.cast(tf.equal(x1, x1_safe), 'float32')
77 # wt_y0 = (y1 - coords_y) * tf.cast(tf.equal(y0, y0_safe), 'float32')
78 # wt_y1 = (coords_y - y0) * tf.cast(tf.equal(y1, y1_safe), 'float32')
79
80 wt_x0 = x1_safe - coords_x
81 wt_x1 = coords_x - x0_safe
82 wt_y0 = y1_safe - coords_y
83 wt_y1 = coords_y - y0_safe
84
85 ## indices in the flat image to sample from
86 dim2 = tf.cast(inp_size[2], 'float32')
87 dim1 = tf.cast(inp_size[2] * inp_size[1], 'float32')
88 base = tf.reshape(
89 _repeat(
90 tf.cast(tf.range(coord_size[0]), 'float32') * dim1,
91 coord_size[1] * coord_size[2]),
92 [out_size[0], out_size[1], out_size[2],
93 1]) # tf.reshape(_repeat(tf.cast(tf.range(4), 'float32') * 128 * 416, 128 * 416), [4, 128, 416, 1])
94 # 上面最后得base=Tensor("Reshape_2:0", shape=(4, 128, 416, 1), dtype=float32)。中间有[ 0.][ 53248.][106496.][159744.]四种数。
95 base_y0 = base + y0_safe * dim2
96 base_y1 = base + y1_safe * dim2 # 考虑进有4个batch，所以不同batch要加上不同的基数。
97 idx00 = tf.reshape(x0_safe + base_y0, [-1]) # 加上基数之后构成了四个像素值的索引。
98 idx01 = x0_safe + base_y1
99 idx10 = x1_safe + base_y0
100 idx11 = x1_safe + base_y1
101
102 ## sample from imgs
103 imgs_flat = tf.reshape(imgs, tf.stack([-1, inp_size[3]]))
104 imgs_flat = tf.cast(imgs_flat, 'float32')
105 im00 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx00, 'int32')), out_size) # 每一个输出都有对应的四个像素点的值参与运算。
106 im01 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx01, 'int32')), out_size)
107 im10 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx10, 'int32')), out_size)
108 im11 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx11, 'int32')), out_size)
109
110 w00 = wt_x0 * wt_y0 ######这里横轴和纵轴的距离乘机就算距离了。
111 w01 = wt_x0 * wt_y1
112 w10 = wt_x1 * wt_y0
113 w11 = wt_x1 * wt_y1
114
115 output = tf.add_n([
116 w00 * im00, w01 * im01,
117 w10 * im10, w11 * im11
118 ])
119
120 # 以下为自定义代码
121 batch, height, width, channels = imgs.get_shape().as_list()
122 cam_coords = cam_coords[:, :, :, 0:-1]
123 cam_coords = tf.cast(cam_coords, 'float32')
124 euclidean = tf.sqrt(tf.reduce_sum(tf.square(cam_coords), 3))
125 euclidean = tf.reshape(euclidean, [2, -1])
126
127 xy00 = tf.concat([x0, y0], axis=3)
128 for i in range(batch):
129 cam_coordsi = cam_coords[i, :, :, :]
130 euclideani = euclidean[i, :]
131 euclideani = tf.reshape(euclideani, [-1, 1])
132 xy00_batchi = xy00[i, :, :, :] # 将横纵坐标合在一起，取batch1.
133 xy00_batchi = tf.reshape(xy00_batchi, [-1, 2])
134 xy00_batchi = tf.cast(xy00_batchi, tf.int32)
135 xy10_batchi = xy00_batchi + [1, 0]
136 xy01_batchi = xy00_batchi + [0, 1]
137 xy11_batchi = xy00_batchi + [1, 1]
138
139 mask0 = tf.ones(shape=[height * width], dtype='float32')
140
141 def true_1():
142 h = tf.cond(pred=tf.less(euclideani[h2, 0], euclideani[h1, 0]), true_fn=lambda: h1, false_fn=lambda: h2)
143 one_hot_true = tf.one_hot(indices=h, depth=12, axis=0)
144 return one_hot_true
145
146 def false_1():
147 one_hot_false = tf.zeros([tf.shape(mask0)[0]])
148 return one_hot_false
149
150 for h1 in range(xy00_batchi.get_shape().as_list()[0] - 1):
151 for h2 in range(h1 + 1, xy00_batchi.get_shape().as_list()[0]):
152 one_hot = tf.cond(pred=tf.reduce_all(tf.equal(xy00_batchi[h1, :], xy00_batchi[h2, :])), true_fn=true_1,
153 false_fn=false_1)
154 mask0 = mask0 - one_hot
155 mask0 = tf.clip_by_value(mask0, 0, 1)
156 mask0 = tf.reshape(mask0, [height, width])
157 mask1 = np.zeros(shape=[height, width], dtype='float32')
158 for l1 in range(xy00_batchi.get_shape().as_list()[0]):
159 q001 = xy00_batchi[l1, 0]
160 q002 = xy00_batchi[l1, 1]
161 q101 = xy10_batchi[l1, 0]
162 q102 = xy10_batchi[l1, 1]
163 q011 = xy01_batchi[l1, 0]
164 q012 = xy01_batchi[l1, 1]
165 q111 = xy11_batchi[l1, 0]
166 q112 = xy11_batchi[l1, 1]
167 var001 = tf.one_hot(indices=q002, depth=width, on_value=q001, off_value=height, axis=-1)
168 var002 = tf.one_hot(indices=var001, depth=height, axis=0)
169 var101 = tf.one_hot(indices=q102, depth=width, on_value=q101, off_value=height, axis=-1)
170 var102 = tf.one_hot(indices=var101, depth=height, axis=0)
171 var011 = tf.one_hot(indices=q012, depth=width, on_value=q011, off_value=height, axis=-1)
172 var012 = tf.one_hot(indices=var011, depth=height, axis=0)
173 var111 = tf.one_hot(indices=q112, depth=width, on_value=q111, off_value=height, axis=-1)
174 var112 = tf.one_hot(indices=var111, depth=height, axis=0)
175 mask1 = mask1 + var002 + var102 + var012 + var112
176 mask1 = tf.clip_by_value(mask1, 0, 1)
177 if i == 0:
178 mask0_stack = mask0
179 mask1_stack = mask1
180 else:
181 mask0_stack = tf.stack([mask0_stack, mask0], axis=0)
182 mask1_stack = tf.stack([mask1_stack, mask1], axis=0)
183 return output, mask0_stack, mask1_stack
184
185
186 output_img, mask0_stack, mask1_stack = bilinear_sampler(imgs, coords, cam_coords)
187 with tf.Session() as sess:
188 # print(output_img)
189 # print(sess.run(output))
190 print(sess.run(output_img))
191 print(sess.run(mask0_stack))
192 print(sess.run(mask1_stack))
193 print(mask0_stack)
194 print(mask1_stack)
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