preprocessing.py 文件源码

def _distort_image(self, image):
    """Distort one image for training a network.

    Adopted the standard data augmentation scheme that is widely used for
    this dataset: the images are first zero-padded with 4 pixels on each side,
    then randomly cropped to again produce distorted images; half of the images
    are then horizontally mirrored.

    Args:
      image: input image.
    Returns:
      distored image.
    """
    image = tf.image.resize_image_with_crop_or_pad(
        image, self.height + 8, self.width + 8)
    distorted_image = tf.random_crop(image,
                                     [self.height, self.width, self.depth])
    # Randomly flip the image horizontally.
    distorted_image = tf.image.random_flip_left_right(distorted_image)
    if self.summary_verbosity >= 3:
      tf.summary.image('distorted_image', tf.expand_dims(distorted_image, 0))
    return distorted_image