# 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 data for the ImageNet ILSVRC 2012 Dataset plus some bounding boxes.

Some images have one or more bounding boxes associated with the label of the
image. See details here: http://image-net.org/download-bboxes

ImageNet is based upon WordNet 3.0. To uniquely identify a synset, we use
"WordNet ID" (wnid), which is a concatenation of POS ( i.e. part of speech )
and SYNSET OFFSET of WordNet. For more information, please refer to the
WordNet documentation[http://wordnet.princeton.edu/wordnet/documentation/].

"There are bounding boxes for over 3000 popular synsets available.
For each synset, there are on average 150 images with bounding boxes."

WARNING: Don't use for object detection, in this case all the bounding boxes
of the image belong to just one class.

To read about optimizations that can be applied to the input preprocessing
stage, see: https://www.tensorflow.org/performance/performance_guide#input_pipeline_optimization.
"""

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

import os
from six.moves import urllib
import tensorflow as tf

import imagenet_dataset_utils

slim = tf.contrib.slim

# TODO(nsilberman): Add tfrecord file type once the script is updated.
_FILE_PATTERN = '%s-*'

_SPLITS_TO_SIZES = {
    'train': 1281167,
    'validation': 50000,
}

_ITEMS_TO_DESCRIPTIONS = {
    'image': 'A color image of varying height and width.',
    'label': 'The label id of the image, integer between 0 and 999',
    'label_text': 'The text of the label.',
    'object/bbox': 'A list of bounding boxes.',
    'object/label': 'A list of labels, one per each object.',
}

_NUM_CLASSES = 1001


def create_readable_names_for_imagenet_labels():
  """Create a dict mapping label id to human readable string.

  Returns:
      labels_to_names: dictionary where keys are integers from to 1000
      and values are human-readable names.

  We retrieve a synset file, which contains a list of valid synset labels used
  by ILSVRC competition. There is one synset one per line, eg.
          #   n01440764
          #   n01443537
  We also retrieve a synset_to_human_file, which contains a mapping from synsets
  to human-readable names for every synset in Imagenet. These are stored in a
  tsv format, as follows:
          #   n02119247    black fox
          #   n02119359    silver fox
  We assign each synset (in alphabetical order) an integer, starting from 1
  (since 0 is reserved for the background class).

  Code is based on
  https://github.com/tensorflow/models/blob/master/research/slim/datasets/build_imagenet_data.py
  """

  # pylint: disable=g-line-too-long
  base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/'
  # pylint: enable=g-line-too-long
  synset_url = '{}/imagenet_lsvrc_2015_synsets.txt'.format(base_url)
  synset_to_human_url = '{}/imagenet_metadata.txt'.format(base_url)

  filename, _ = urllib.request.urlretrieve(synset_url)
  synset_list = [s.strip() for s in open(filename).readlines()]
  num_synsets_in_ilsvrc = len(synset_list)
  assert num_synsets_in_ilsvrc == 1000

  filename, _ = urllib.request.urlretrieve(synset_to_human_url)
  synset_to_human_list = open(filename).readlines()
  num_synsets_in_all_imagenet = len(synset_to_human_list)
  assert num_synsets_in_all_imagenet == 21842

  synset_to_human = {}
  for s in synset_to_human_list:
    parts = s.strip().split('\t')
    assert len(parts) == 2
    synset = parts[0]
    human = parts[1]
    synset_to_human[synset] = human

  label_index = 1
  labels_to_names = {0: 'background'}
  for synset in synset_list:
    name = synset_to_human[synset]
    labels_to_names[label_index] = name
    label_index += 1

  return labels_to_names


def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
  """Gets a dataset tuple with instructions for reading ImageNet.

  Args:
    split_name: A train/test split name.
    dataset_dir: The base directory of the dataset sources.
    file_pattern: The file pattern to use when matching the dataset sources.
      It is assumed that the pattern contains a '%s' string so that the split
      name can be inserted.
    reader: The TensorFlow reader type.

  Returns:
    A `Dataset` namedtuple.

  Raises:
    ValueError: if `split_name` is not a valid train/test split.
  """
  if split_name not in _SPLITS_TO_SIZES:
    raise ValueError('split name %s was not recognized.' % split_name)

  if not file_pattern:
    file_pattern = _FILE_PATTERN
  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)

  # Allowing None in the signature so that dataset_factory can use the default.
  if reader is None:
    reader = tf.TFRecordReader

  keys_to_features = {
      'image/encoded': tf.FixedLenFeature(
          (), tf.string, default_value=''),
      'image/format': tf.FixedLenFeature(
          (), tf.string, default_value='jpeg'),
      'image/class/label': tf.FixedLenFeature(
          [], dtype=tf.int64, default_value=-1),
      'image/class/text': tf.FixedLenFeature(
          [], dtype=tf.string, default_value=''),
      'image/object/bbox/xmin': tf.VarLenFeature(
          dtype=tf.float32),
      'image/object/bbox/ymin': tf.VarLenFeature(
          dtype=tf.float32),
      'image/object/bbox/xmax': tf.VarLenFeature(
          dtype=tf.float32),
      'image/object/bbox/ymax': tf.VarLenFeature(
          dtype=tf.float32),
      'image/object/class/label': tf.VarLenFeature(
          dtype=tf.int64),
  }

  items_to_handlers = {
      'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
      'label': slim.tfexample_decoder.Tensor('image/class/label'),
      'label_text': slim.tfexample_decoder.Tensor('image/class/text'),
      'object/bbox': slim.tfexample_decoder.BoundingBox(
          ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
      'object/label': slim.tfexample_decoder.Tensor('image/object/class/label'),
  }

  decoder = slim.tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handlers)

  labels_to_names = None
  if imagenet_dataset_utils.has_labels(dataset_dir):
    labels_to_names = imagenet_dataset_utils.read_label_file(dataset_dir)
  else:
    labels_to_names = create_readable_names_for_imagenet_labels()
    imagenet_dataset_utils.write_label_file(labels_to_names, dataset_dir)

  return slim.dataset.Dataset(
      data_sources=file_pattern,
      reader=reader,
      decoder=decoder,
      num_samples=_SPLITS_TO_SIZES[split_name],
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
      num_classes=_NUM_CLASSES,
      labels_to_names=labels_to_names)