# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""Read and preprocess image data.

 Image processing occurs on a single image at a time. Image are read and
 preprocessed in parallel across multiple threads. The resulting images
 are concatenated together to form a single batch for training or evaluation.

 -- Provide processed image data for a network:
 inputs: Construct batches of evaluation examples of images.
 distorted_inputs: Construct batches of training examples of images.
 batch_inputs: Construct batches of training or evaluation examples of images.

 -- Data processing:
 parse_example_proto: Parses an Example proto containing a training example
   of an image.

 -- Image decoding:
 decode_jpeg: Decode a JPEG encoded string into a 3-D float32 Tensor.

 -- Image preprocessing:
 image_preprocessing: Decode and preprocess one image for evaluation or training
 distort_image: Distort one image for training a network.
 eval_image: Prepare one image for evaluation.
 distort_color: Distort the color in one image for training.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_integer('batch_size', 32,
                            """Number of images to process in a batch.""")
tf.app.flags.DEFINE_integer('image_size', 224,
                            """Provide square images of this size.""")
tf.app.flags.DEFINE_integer('num_preprocess_threads', 4,
                            """Number of preprocessing threads per tower. """
                            """Please make this a multiple of 4.""")
tf.app.flags.DEFINE_integer('num_readers', 4,
                            """Number of parallel readers during train.""")

# Images are preprocessed asynchronously using multiple threads specified by
# --num_preprocss_threads and the resulting processed images are stored in a
# random shuffling queue. The shuffling queue dequeues --batch_size images
# for processing on a given Inception tower. A larger shuffling queue guarantees
# better mixing across examples within a batch and results in slightly higher
# predictive performance in a trained model. Empirically,
# --input_queue_memory_factor=16 works well. A value of 16 implies a queue size
# of 1024*16 images. Assuming RGB 299x299 images, this implies a queue size of
# 16GB. If the machine is memory limited, then decrease this factor to
# decrease the CPU memory footprint, accordingly.
tf.app.flags.DEFINE_integer('input_queue_memory_factor', 16,
                            """Size of the queue of preprocessed images. """
                            """Default is ideal but try smaller values, e.g. """
                            """4, 2 or 1, if host memory is constrained. See """
                            """comments in code for more details.""")


def inputs(dataset, cycle=False, batch_size=None, num_preprocess_threads=None):
  """Generate batches of ImageNet images for evaluation.

  Use this function as the inputs for evaluating a network.

  Note that some (minimal) image preprocessing occurs during evaluation
  including central cropping and resizing of the image to fit the network.

  Args:
    dataset: instance of Dataset class specifying the dataset.
    batch_size: integer, number of examples in batch
    num_preprocess_threads: integer, total number of preprocessing threads but
      None defaults to FLAGS.num_preprocess_threads.

  Returns:
    images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
                                       image_size, 3].
    labels: 1-D integer Tensor of [FLAGS.batch_size].
  """
  if not batch_size:
    batch_size = FLAGS.batch_size

  # Force all input processing onto CPU in order to reserve the GPU for
  # the forward inference and back-propagation.
  with tf.device('/cpu:0'):
    images, labels = batch_inputs(
        dataset,
        batch_size,
        cycle=cycle,
        train=False,
        num_preprocess_threads=num_preprocess_threads,
        num_readers=1)

  return images, labels


def distorted_inputs(dataset,
                     batch_size=None,
                     num_preprocess_threads=None,
                     seed=0):
  """Generate batches of distorted versions of ImageNet images.

  Use this function as the inputs for training a network.

  Distorting images provides a useful technique for augmenting the data
  set during training in order to make the network invariant to aspects
  of the image that do not effect the label.

  Args:
    dataset: instance of Dataset class specifying the dataset.
    batch_size: integer, number of examples in batch
    num_preprocess_threads: integer, total number of preprocessing threads but
      None defaults to FLAGS.num_preprocess_threads.

  Returns:
    images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
                                       FLAGS.image_size, 3].
    labels: 1-D integer Tensor of [batch_size].
  """
  if not batch_size:
    batch_size = FLAGS.batch_size

  # Force all input processing onto CPU in order to reserve the GPU for
  # the forward inference and back-propagation.
  with tf.device('/cpu:0'):
    images, labels = batch_inputs(
        dataset,
        batch_size,
        train=True,
        num_preprocess_threads=num_preprocess_threads,
        num_readers=FLAGS.num_readers,
        seed=seed)
  return images, labels


def decode_jpeg(image_buffer, scope=None):
  """Decode a JPEG string into one 3-D float image Tensor.

  Args:
    image_buffer: scalar string Tensor.
    scope: Optional scope for name_scope.
  Returns:
    3-D float Tensor with values ranging from [0, 1).
  """
  with tf.name_scope(
      values=[image_buffer], name=scope, default_name='decode_jpeg'):
    # Decode the string as an RGB JPEG.
    # Note that the resulting image contains an unknown height and width
    # that is set dynamically by decode_jpeg. In other words, the height
    # and width of image is unknown at compile-time.
    image = tf.image.decode_jpeg(image_buffer, channels=3)

    # After this point, all image pixels reside in [0,1)
    # until the very end, when they're rescaled to (-1, 1).  The various
    # adjust_* ops all require this range for dtype float.
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)
    return image


def distort_color(image, thread_id=0, scope=None):
  """Distort the color of the image.

  Each color distortion is non-commutative and thus ordering of the color ops
  matters. Ideally we would randomly permute the ordering of the color ops.
  Rather then adding that level of complication, we select a distinct ordering
  of color ops for each preprocessing thread.

  Args:
    image: Tensor containing single image.
    thread_id: preprocessing thread ID.
    scope: Optional scope for name_scope.
  Returns:
    color-distorted image
  """
  with tf.name_scope(values=[image], name=scope, default_name='distort_color'):
    color_ordering = thread_id % 2

    if color_ordering == 0:
      image = tf.image.random_brightness(image, max_delta=32. / 255.)
      image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
      image = tf.image.random_hue(image, max_delta=0.2)
      image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
    elif color_ordering == 1:
      image = tf.image.random_brightness(image, max_delta=32. / 255.)
      image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
      image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
      image = tf.image.random_hue(image, max_delta=0.2)

    # The random_* ops do not necessarily clamp.
    image = tf.clip_by_value(image, 0.0, 1.0)
    return image


def distort_image(image, height, width, bbox, thread_id=0, scope=None):
  """Distort one image for training a network.

  Distorting images provides a useful technique for augmenting the data
  set during training in order to make the network invariant to aspects
  of the image that do not effect the label.

  Args:
    image: 3-D float Tensor of image
    height: integer
    width: integer
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged
      as [ymin, xmin, ymax, xmax].
    thread_id: integer indicating the preprocessing thread.
    scope: Optional scope for name_scope.
  Returns:
    3-D float Tensor of distorted image used for training.
  """
  with tf.name_scope(
      values=[image, height, width, bbox],
      name=scope,
      default_name='distort_image'):
    # Each bounding box has shape [1, num_boxes, box coords] and
    # the coordinates are ordered [ymin, xmin, ymax, xmax].
    # A large fraction of image datasets contain a human-annotated bounding
    # box delineating the region of the image containing the object of interest.
    # We choose to create a new bounding box for the object which is a randomly
    # distorted version of the human-annotated bounding box that obeys an allowed
    # range of aspect ratios, sizes and overlap with the human-annotated
    # bounding box. If no box is supplied, then we assume the bounding box is
    # the entire image.
    sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
        tf.shape(image),
        bounding_boxes=bbox,
        min_object_covered=0.1,
        aspect_ratio_range=[0.75, 1.33],
        area_range=[0.05, 1.0],
        max_attempts=100,
        use_image_if_no_bounding_boxes=True)
    bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box
    if not thread_id:
      image_with_distorted_box = tf.image.draw_bounding_boxes(
          tf.expand_dims(image, 0), distort_bbox)
      tf.summary.image('images_with_distorted_bounding_box',
                       image_with_distorted_box)

    # Crop the image to the specified bounding box.
    distorted_image = tf.slice(image, bbox_begin, bbox_size)

    # This resizing operation may distort the images because the aspect
    # ratio is not respected. We select a resize method in a round robin
    # fashion based on the thread number.
    # Note that ResizeMethod contains 4 enumerated resizing methods.
    resize_method = thread_id % 4
    distorted_image = tf.image.resize_images(
        distorted_image, [height, width], method=resize_method)
    # Restore the shape since the dynamic slice based upon the bbox_size loses
    # the third dimension.
    distorted_image.set_shape([height, width, 3])

    # Randomly flip the image horizontally.
    distorted_image = tf.image.random_flip_left_right(distorted_image)

    # Randomly distort the colors.
    distorted_image = distort_color(distorted_image, thread_id)
    return distorted_image


def eval_image(image, height, width, scope=None):
  """Prepare one image for evaluation.

  Args:
    image: 3-D float Tensor
    height: integer
    width: integer
    scope: Optional scope for name_scope.
  Returns:
    3-D float Tensor of prepared image.
  """
  with tf.name_scope(
      values=[image, height, width], name=scope, default_name='eval_image'):
    # Crop the central region of the image with an area containing 87.5% of
    # the original image.
    image = tf.image.central_crop(image, central_fraction=0.875)

    # Resize the image to the original height and width.
    image = tf.expand_dims(image, 0)
    image = tf.image.resize_bilinear(
        image, [height, width], align_corners=False)
    image = tf.squeeze(image, [0])
    return image


def image_preprocessing(image_buffer, bbox, train, thread_id=0):
  """Decode and preprocess one image for evaluation or training.

  Args:
    image_buffer: JPEG encoded string Tensor
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged as
      [ymin, xmin, ymax, xmax].
    train: boolean
    thread_id: integer indicating preprocessing thread

  Returns:
    3-D float Tensor containing an appropriately scaled image

  Raises:
    ValueError: if user does not provide bounding box
  """
  if bbox is None:
    raise ValueError('Please supply a bounding box.')

  image = decode_jpeg(image_buffer)
  height = FLAGS.image_size
  width = FLAGS.image_size

  if train:
    image = distort_image(image, height, width, bbox, thread_id)
  else:
    image = eval_image(image, height, width)

  # Finally, rescale to [-1,1] instead of [0, 1)
  image = tf.subtract(image, 0.5)
  image = tf.multiply(image, 2.0)
  return image


def parse_example_proto(example_serialized):
  """Parses an Example proto containing a training example of an image.

  The output of the build_image_data.py image preprocessing script is a dataset
  containing serialized Example protocol buffers. Each Example proto contains
  the following fields:

    image/height: 462
    image/width: 581
    image/colorspace: 'RGB'
    image/channels: 3
    image/class/label: 615
    image/class/synset: 'n03623198'
    image/class/text: 'knee pad'
    image/object/bbox/xmin: 0.1
    image/object/bbox/xmax: 0.9
    image/object/bbox/ymin: 0.2
    image/object/bbox/ymax: 0.6
    image/object/bbox/label: 615
    image/format: 'JPEG'
    image/filename: 'ILSVRC2012_val_00041207.JPEG'
    image/encoded: <JPEG encoded string>

  Args:
    example_serialized: scalar Tensor tf.string containing a serialized
      Example protocol buffer.

  Returns:
    image_buffer: Tensor tf.string containing the contents of a JPEG file.
    label: Tensor tf.int32 containing the label.
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged as
      [ymin, xmin, ymax, xmax].
    text: Tensor tf.string containing the human-readable label.
  """
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.FixedLenFeature(
          [], dtype=tf.string, default_value=''),
      'image/class/label': tf.FixedLenFeature(
          [1], dtype=tf.int64, default_value=-1),
      'image/class/text': tf.FixedLenFeature(
          [], dtype=tf.string, default_value=''),
  }
  sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
  # Sparse features in Example proto.
  feature_map.update({
      k: sparse_float32
      for k in [
          'image/object/bbox/xmin', 'image/object/bbox/ymin',
          'image/object/bbox/xmax', 'image/object/bbox/ymax'
      ]
  })

  features = tf.parse_single_example(example_serialized, feature_map)
  label = tf.cast(features['image/class/label'], dtype=tf.int32)

  xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)
  ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0)
  xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0)
  ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0)

  # Note that we impose an ordering of (y, x) just to make life difficult.
  bbox = tf.concat(axis=0, values=[ymin, xmin, ymax, xmax])

  # Force the variable number of bounding boxes into the shape
  # [1, num_boxes, coords].
  bbox = tf.expand_dims(bbox, 0)
  bbox = tf.transpose(bbox, [0, 2, 1])

  return features['image/encoded'], label, bbox, features['image/class/text']


def batch_inputs(dataset,
                 batch_size,
                 train,
                 num_preprocess_threads=None,
                 cycle=True,
                 num_readers=1,
                 seed=0):
  """Contruct batches of training or evaluation examples from the image dataset.

  Args:
    dataset: instance of Dataset class specifying the dataset.
      See dataset.py for details.
    batch_size: integer
    train: boolean
    num_preprocess_threads: integer, total number of preprocessing threads
    num_readers: integer, number of parallel readers

  Returns:
    images: 4-D float Tensor of a batch of images
    labels: 1-D integer Tensor of [batch_size].

  Raises:
    ValueError: if data is not found
  """
  with tf.name_scope('batch_processing'):
    data_files = dataset.data_files()
    if data_files is None:
      raise ValueError('No data files found for this dataset')

    if cycle:
      num_epochs = None
    else:
      num_epochs = 1

    # Create filename_queue
    if train:
      filename_queue = tf.train.string_input_producer(
          data_files,
          shuffle=True,
          capacity=16,
          num_epochs=num_epochs,
          seed=seed)
    else:
      filename_queue = tf.train.string_input_producer(
          data_files, shuffle=False, capacity=1, num_epochs=num_epochs)
    if num_preprocess_threads is None:
      num_preprocess_threads = FLAGS.num_preprocess_threads

    if num_preprocess_threads % 4:
      raise ValueError('Please make num_preprocess_threads a multiple '
                       'of 4 (%d % 4 != 0).', num_preprocess_threads)

    if num_readers is None:
      num_readers = FLAGS.num_readers

    if num_readers < 1:
      raise ValueError('Please make num_readers at least 1')

    # Approximate number of examples per shard.
    examples_per_shard = 1024
    # Size the random shuffle queue to balance between good global
    # mixing (more examples) and memory use (fewer examples).
    # 1 image uses 299*299*3*4 bytes = 1MB
    # The default input_queue_memory_factor is 16 implying a shuffling queue
    # size: examples_per_shard * 16 * 1MB = 17.6GB
    min_queue_examples = examples_per_shard * FLAGS.input_queue_memory_factor
    if train:
      examples_queue = tf.RandomShuffleQueue(
          capacity=min_queue_examples + 3 * batch_size,
          min_after_dequeue=min_queue_examples,
          dtypes=[tf.string],
          seed=seed)
    else:
      examples_queue = tf.FIFOQueue(
          capacity=examples_per_shard + 3 * batch_size, dtypes=[tf.string])

    # Create multiple readers to populate the queue of examples.
    if num_readers > 1:
      enqueue_ops = []
      for _ in range(num_readers):
        reader = dataset.reader()
        _, value = reader.read(filename_queue)
        enqueue_ops.append(examples_queue.enqueue([value]))

      tf.train.queue_runner.add_queue_runner(
          tf.train.queue_runner.QueueRunner(examples_queue, enqueue_ops))
      example_serialized = examples_queue.dequeue()
    else:
      reader = dataset.reader()
      _, example_serialized = reader.read(filename_queue)

    images_and_labels = []
    for thread_id in range(num_preprocess_threads):
      # Parse a serialized Example proto to extract the image and metadata.
      image_buffer, label_index, bbox, _ = parse_example_proto(
          example_serialized)
      image = image_preprocessing(image_buffer, bbox, train, thread_id)
      images_and_labels.append([image, label_index])

    images, label_index_batch = tf.train.batch_join(
        images_and_labels,
        batch_size=batch_size,
        capacity=2 * num_preprocess_threads * batch_size)

    # Reshape images into these desired dimensions.
    height = FLAGS.image_size
    width = FLAGS.image_size
    depth = 3

    images = tf.cast(images, tf.float32)
    images = tf.reshape(images, shape=[batch_size, height, width, depth])

    # Display the training images in the visualizer.
    tf.summary.image('images', images)
    label_index_batch -= 1  # 1-based to 0-based.

    return images, tf.reshape(label_index_batch, [batch_size])
