# Trimmed by Vasil Zlatanov
# Copyright 2018 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.
# ==============================================================================
"""ImageNet preprocessing for ResNet."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

def resize_or_crop_image(image, target_height, target_width):
  image_height = tf.shape(image)[0]
  image_width = tf.shape(image)[1]
  # If the viewport is long but the width is right simply crop the length of the page
  # Otherwise we just resize the image bilinearly
  image = tf.cond(
          tf.logical_and(tf.greater(image_height, target_height),tf.equal(target_width, image_width)),
          lambda: tf.cast(tf.image.crop_to_bounding_box(image, 0, 0, target_height, target_width),dtype=tf.float32),
          lambda: tf.image.resize_images(image, [target_height,target_width], align_corners=True)
          )
  return image

def preprocess_for_train(image_bytes, target_width, target_height, resize, use_bfloat16):
  """Preprocesses the given image for evaluation.

  Args:
    image_bytes: `Tensor` representing an image binary of arbitrary size.
    use_bfloat16: `bool` for whether to use bfloat16.

  Returns:
    A preprocessed image `Tensor`.
  """
  image = tf.image.decode_png(image_bytes, channels=3)
  if resize:
      image = resize_or_crop_image(image, target_height, target_width)
  else:
      image = tf.cast(image, tf.float32)

  return image


def preprocess_for_eval(image_bytes, target_width, target_height, resize, use_bfloat16):
  """Preprocesses the given image for evaluation.

  Args:
    image_bytes: `Tensor` representing an image binary of arbitrary size.
    use_bfloat16: `bool` for whether to use bfloat16.

  Returns:
    A preprocessed image `Tensor`.
  """
  image = tf.image.decode_png(image_bytes, channels=3)
  if resize:
      image = resize_or_crop_image(image, target_height, target_width)
  else:
      image = tf.cast(image, tf.float32)

  return image


def preprocess_image(image_bytes, width, height, resize, is_training=False, use_bfloat16=False):
  """Preprocesses the given image.

  Args:
    image_bytes: `Tensor` representing an image binary of arbitrary size.
    is_training: `bool` for whether the preprocessing is for training.
    use_bfloat16: `bool` for whether to use bfloat16.

  Returns:
    A preprocessed image `Tensor`.
  """
  if is_training:
    return preprocess_for_train(image_bytes, width, height, resize, use_bfloat16)
  else:
    return preprocess_for_eval(image_bytes, width, height, resize, use_bfloat16)