input.py 文件源码

def data_augmentation_fn(input_image: tf.Tensor, label_image: tf.Tensor) -> (tf.Tensor, tf.Tensor):
    with tf.name_scope('DataAugmentation'):
        with tf.name_scope('random_flip_lr'):
            sample = tf.random_uniform([], 0, 1)
            label_image = tf.cond(sample > 0.5, lambda: tf.image.flip_left_right(label_image), lambda: label_image)
            input_image = tf.cond(sample > 0.5, lambda: tf.image.flip_left_right(input_image), lambda: input_image)
        with tf.name_scope('random_flip_ud'):
            sample = tf.random_uniform([], 0, 1)
            label_image = tf.cond(sample > 0.5, lambda: tf.image.flip_up_down(label_image), lambda: label_image)
            input_image = tf.cond(sample > 0.5, lambda: tf.image.flip_up_down(input_image), lambda: input_image)

        chanels = input_image.get_shape()[-1]
        input_image = tf.image.random_contrast(input_image, lower=0.8, upper=1.0)
        if chanels == 3:
            input_image = tf.image.random_hue(input_image, max_delta=0.1)
            input_image = tf.image.random_saturation(input_image, lower=0.8, upper=1.2)
        return input_image, label_image