# Copyright 2017 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
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# ==============================================================================

"""Label map utility functions."""

import logging

import tensorflow as tf
from google.protobuf import text_format
from object_detection.protos import string_int_label_map_pb2


def create_category_index(categories):
  """Creates dictionary of COCO compatible categories keyed by category id.

  Args:
    categories: a list of dicts, each of which has the following keys:
      'id': (required) an integer id uniquely identifying this category.
      'name': (required) string representing category name
        e.g., 'cat', 'dog', 'pizza'.

  Returns:
    category_index: a dict containing the same entries as categories, but keyed
      by the 'id' field of each category.
  """
  category_index = {}
  for cat in categories:
    category_index[cat['id']] = cat
  return category_index


def convert_label_map_to_categories(label_map,
                                    max_num_classes,
                                    use_display_name=True):
  """Loads label map proto and returns categories list compatible with eval.

  This function loads a label map and returns a list of dicts, each of which
  has the following keys:
    'id': (required) an integer id uniquely identifying this category.
    'name': (required) string representing category name
      e.g., 'cat', 'dog', 'pizza'.
  We only allow class into the list if its id-label_id_offset is
  between 0 (inclusive) and max_num_classes (exclusive).
  If there are several items mapping to the same id in the label map,
  we will only keep the first one in the categories list.

  Args:
    label_map: a StringIntLabelMapProto or None.  If None, a default categories
      list is created with max_num_classes categories.
    max_num_classes: maximum number of (consecutive) label indices to include.
    use_display_name: (boolean) choose whether to load 'display_name' field
      as category name.  If False of if the display_name field does not exist,
      uses 'name' field as category names instead.
  Returns:
    categories: a list of dictionaries representing all possible categories.
  """
  categories = []
  list_of_ids_already_added = []
  if not label_map:
    label_id_offset = 1
    for class_id in range(max_num_classes):
      categories.append({
          'id': class_id + label_id_offset,
          'name': 'category_{}'.format(class_id + label_id_offset)
      })
    return categories
  for item in label_map.item:
    if not 0 < item.id <= max_num_classes:
      logging.info('Ignore item %d since it falls outside of requested '
                   'label range.', item.id)
      continue
    if use_display_name and item.HasField('display_name'):
      name = item.display_name
    else:
      name = item.name
    if item.id not in list_of_ids_already_added:
      list_of_ids_already_added.append(item.id)
      categories.append({'id': item.id, 'name': name})
  return categories


# TODO: double check documentaion.
def load_labelmap(path):
  """Loads label map proto.

  Args:
    path: path to StringIntLabelMap proto text file.
  Returns:
    a StringIntLabelMapProto
  """
  with tf.gfile.GFile(path, 'r') as fid:
    label_map_string = fid.read()
    label_map = string_int_label_map_pb2.StringIntLabelMap()
    try:
      text_format.Merge(label_map_string, label_map)
    except text_format.ParseError:
      label_map.ParseFromString(label_map_string)
  return label_map


def get_label_map_dict(label_map_path):
  """Reads a label map and returns a dictionary of label names to id.

  Args:
    label_map_path: path to label_map.

  Returns:
    A dictionary mapping label names to id.
  """
  label_map = load_labelmap(label_map_path)
  label_map_dict = {}
  for item in label_map.item:
    label_map_dict[item.name] = item.id
  return label_map_dict
