Skip to content
Snippets Groups Projects
Select Git revision
  • master default protected
  • use_flight_altitude
  • distribute
3 results
Blame Permalink
augment.py 4.56 KiB
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 






from functools import partial

import tensorflow as tf


def augment_image():
    """ Helper function used for augmentation of images in the dataset.
      Returns:
        tf.data.Dataset mappable function for image augmentation
    """

    def augment_fn(data, label, *args, **kwargs):
        # Augmenting data (~ 80%)

        def augment_steps_fn(data, label):
            # Randomly rotating image (~50%)
            def rotate_fn(data, label):
                times = tf.random.uniform(minval=1, maxval=4, dtype=tf.int32, shape=[])
                return (tf.image.rot90(data, times),
                        tf.image.rot90(label, times))

            data, label = tf.cond(
                tf.less_equal(tf.random.uniform([]), 0.5),
                lambda: rotate_fn(data, label),
                lambda: (data, label))

            # Randomly flipping image (~50%)
            def flip_fn(data, label):
                return (tf.image.flip_left_right(data),
                        tf.image.flip_left_right(label))

            data, label = tf.cond(
                tf.less_equal(tf.random.uniform([]), 0.5),
                lambda: flip_fn(data, label),
                lambda: (data, label))

            return data, label

        # Randomly returning unchanged data (~20%)
        return tf.cond(
            tf.less_equal(tf.random.uniform([]), 0.2),
            lambda: (data, label),
            partial(augment_steps_fn, data, label))

    return augment_fn


def augment_icing():
    """ Helper function used for augmentation of images in the dataset.
      Returns:
        tf.data.Dataset mappable function for image augmentation
    """

    def augment_fn(data, data_b, label, *args, **kwargs):
        # Augmenting data (~ 80%)

        def augment_steps_fn(data, data_b, label):
            # Randomly rotating image (~50%)
            def rotate_fn(data, data_b, label):
                times = tf.random.uniform(minval=1, maxval=4, dtype=tf.int32, shape=[])
                return (tf.image.rot90(data, times),
                        data_b,
                        label)

            data, data_b, label = tf.cond(
                tf.less_equal(tf.random.uniform([]), 0.5),
                lambda: rotate_fn(data, data_b, label),
                lambda: (data, data_b, label))

            # Randomly flipping image (~50%)
            def flip_fn(data, data_b, label):
                return (tf.image.flip_left_right(data),
                        data_b,
                        label)

            data, data_b, label = tf.cond(
                tf.less_equal(tf.random.uniform([]), 0.5),
                lambda: flip_fn(data, data_b, label),
                lambda: (data, data_b, label))

            return data, data_b, label

        # Randomly returning unchanged data (~20%)
        return tf.cond(
            tf.less_equal(tf.random.uniform([]), 0.2),
            lambda: (data, data_b, label),
            partial(augment_steps_fn, data, data_b, label))

    return augment_fn


def augment_image_3arg():
    """ Helper function used for augmentation of images in the dataset.
      Returns:
        tf.data.Dataset mappable function for image augmentation
    """

    def augment_fn(data, data_b, label, *args, **kwargs):
        # Augmenting data (~ 80%)

        def augment_steps_fn(data, data_b, label):
            # Randomly rotating image (~50%)
            def rotate_fn(data, data_b, label):
                times = tf.random.uniform(minval=1, maxval=4, dtype=tf.int32, shape=[])
                return (tf.image.rot90(data, times),
                        tf.image.rot90(data_b, times),
                        tf.image.rot90(label, times))

            data, data_b, label = tf.cond(
                tf.less_equal(tf.random.uniform([]), 0.5),
                lambda: rotate_fn(data, data_b, label),
                lambda: (data, data_b, label))

            # Randomly flipping image (~50%)
            def flip_fn(data, data_b, label):
                return (tf.image.flip_left_right(data),
                        tf.image.flip_left_right(data_b),
                        tf.image.flip_left_right(label))

            data, data_b, label = tf.cond(
                tf.less_equal(tf.random.uniform([]), 0.5),
                lambda: flip_fn(data, data_b, label),
                lambda: (data, data_b, label))

            return data, data_b, label

        # Randomly returning unchanged data (~20%)
        return tf.cond(
            tf.less_equal(tf.random.uniform([]), 0.2),
            lambda: (data, data_b, label),
            partial(augment_steps_fn, data, data_b, label))

    return augment_fn