lenet_preprocessing.py

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Provides utilities for preprocessing."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from tensorflow.contrib import slim as contrib_slim

slim = contrib_slim


def preprocess_image(image,
                     output_height,
                     output_width,
                     is_training,
                     use_grayscale=False):
  """Preprocesses the given image.

  Args:
    image: A `Tensor` representing an image of arbitrary size.
    output_height: The height of the image after preprocessing.
    output_width: The width of the image after preprocessing.
    is_training: `True` if we're preprocessing the image for training and
      `False` otherwise.
    use_grayscale: Whether to convert the image from RGB to grayscale.

  Returns:
    A preprocessed image.
  """
  del is_training  # Unused argument
  image = tf.to_float(image)
  if use_grayscale:
    image = tf.image.rgb_to_grayscale(image)
  image = tf.image.resize_image_with_crop_or_pad(
      image, output_width, output_height)
  image = tf.subtract(image, 128.0)
  image = tf.div(image, 128.0)
  return image