vertix

# You need to add your local TF-models slim directory to PYTHON_PATH
# or just run script from this directory.
import tensorflow as tf
from preprocessing import inception_preprocessing

# you can use any uint8 tensor of matching dimensions
single_image = tf.placeholder(tf.uint8, shape=(None, None, 3))

# Augmentation operations are created here
augmented_image = inception_preprocessing.preprocess_image(

vertix

def augment(images, labels,
            resize=None, # (width, height) tuple or None
            horizontal_flip=False,
            vertical_flip=False,
            rotate=0, # Maximum rotation angle in degrees
            crop_probability=0, # How often we do crops
            crop_min_percent=0.6, # Minimum linear dimension of a crop
            crop_max_percent=1.,  # Maximum linear dimension of a crop
            mixup=0):  # Mixup coeffecient, see https://arxiv.org/abs/1710.09412.pdf
  if resize is not None: