# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================
"""Label map utility functions."""

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

import collections
import logging

import numpy as np
from six import string_types
from six.moves import range
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
from object_detection.protos import string_int_label_map_pb2

_LABEL_OFFSET = 1


def _validate_label_map(label_map):
  """Checks if a label map is valid.

  Args:
    label_map: StringIntLabelMap to validate.

  Raises:
    ValueError: if label map is invalid.
  """
  for item in label_map.item:
    if item.id < 0:
      raise ValueError('Label map ids should be >= 0.')
    if (item.id == 0 and item.name != 'background' and
        item.display_name != 'background'):
      raise ValueError('Label map id 0 is reserved for the background label')


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 get_max_label_map_index(label_map):
  """Get maximum index in label map.

  Args:
    label_map: a StringIntLabelMapProto

  Returns:
    an integer
  """
  return max([item.id for item in label_map.item])


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

  This function converts label map proto 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'.
    'keypoints': (optional) a dictionary of keypoint string 'label' to integer
      'id'.
  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 or 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)
      category = {'id': item.id, 'name': name}
      if item.HasField('frequency'):
        if item.frequency == string_int_label_map_pb2.LVISFrequency.Value(
            'FREQUENT'):
          category['frequency'] = 'f'
        elif item.frequency == string_int_label_map_pb2.LVISFrequency.Value(
            'COMMON'):
          category['frequency'] = 'c'
        elif item.frequency == string_int_label_map_pb2.LVISFrequency.Value(
            'RARE'):
          category['frequency'] = 'r'
      if item.HasField('instance_count'):
        category['instance_count'] = item.instance_count
      if item.keypoints:
        keypoints = {}
        list_of_keypoint_ids = []
        for kv in item.keypoints:
          if kv.id in list_of_keypoint_ids:
            raise ValueError('Duplicate keypoint ids are not allowed. '
                             'Found {} more than once'.format(kv.id))
          keypoints[kv.label] = kv.id
          list_of_keypoint_ids.append(kv.id)
        category['keypoints'] = keypoints
      categories.append(category)
  return categories


def load_labelmap(path):
  """Loads label map proto.

  Args:
    path: path to StringIntLabelMap proto text file.
  Returns:
    a StringIntLabelMapProto
  """
  with tf.io.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)
  _validate_label_map(label_map)
  return label_map


def get_label_map_dict(label_map_path_or_proto,
                       use_display_name=False,
                       fill_in_gaps_and_background=False):
  """Reads a label map and returns a dictionary of label names to id.

  Args:
    label_map_path_or_proto: path to StringIntLabelMap proto text file or the
      proto itself.
    use_display_name: whether to use the label map items' display names as keys.
    fill_in_gaps_and_background: whether to fill in gaps and background with
    respect to the id field in the proto. The id: 0 is reserved for the
    'background' class and will be added if it is missing. All other missing
    ids in range(1, max(id)) will be added with a dummy class name
    ("class_<id>") if they are missing.

  Returns:
    A dictionary mapping label names to id.

  Raises:
    ValueError: if fill_in_gaps_and_background and label_map has non-integer or
    negative values.
  """
  if isinstance(label_map_path_or_proto, string_types):
    label_map = load_labelmap(label_map_path_or_proto)
  else:
    _validate_label_map(label_map_path_or_proto)
    label_map = label_map_path_or_proto

  label_map_dict = {}
  for item in label_map.item:
    if use_display_name:
      label_map_dict[item.display_name] = item.id
    else:
      label_map_dict[item.name] = item.id

  if fill_in_gaps_and_background:
    values = set(label_map_dict.values())

    if 0 not in values:
      label_map_dict['background'] = 0
    if not all(isinstance(value, int) for value in values):
      raise ValueError('The values in label map must be integers in order to'
                       'fill_in_gaps_and_background.')
    if not all(value >= 0 for value in values):
      raise ValueError('The values in the label map must be positive.')

    if len(values) != max(values) + 1:
      # there are gaps in the labels, fill in gaps.
      for value in range(1, max(values)):
        if value not in values:
          # TODO(rathodv): Add a prefix 'class_' here once the tool to generate
          # teacher annotation adds this prefix in the data.
          label_map_dict[str(value)] = value

  return label_map_dict


def get_keypoint_label_map_dict(label_map_path_or_proto):
  """Reads a label map and returns a dictionary of keypoint names to ids.

  Note that the keypoints belong to different classes will be merged into a
  single dictionary. It is expected that there is no duplicated keypoint names
  or ids from different classes.

  Args:
    label_map_path_or_proto: path to StringIntLabelMap proto text file or the
      proto itself.

  Returns:
    A dictionary mapping keypoint names to the keypoint id (not the object id).

  Raises:
    ValueError: if there are duplicated keyoint names or ids.
  """
  if isinstance(label_map_path_or_proto, string_types):
    label_map = load_labelmap(label_map_path_or_proto)
  else:
    label_map = label_map_path_or_proto

  label_map_dict = {}
  for item in label_map.item:
    for kpts in item.keypoints:
      if kpts.label in label_map_dict.keys():
        raise ValueError('Duplicated keypoint label: %s' % kpts.label)
      if kpts.id in label_map_dict.values():
        raise ValueError('Duplicated keypoint ID: %d' % kpts.id)
      label_map_dict[kpts.label] = kpts.id
  return label_map_dict


def get_label_map_hierarchy_lut(label_map_path_or_proto,
                                include_identity=False):
  """Reads a label map and returns ancestors and descendants in the hierarchy.

  The function returns the ancestors and descendants as separate look up tables
   (LUT) numpy arrays of shape [max_id, max_id] where lut[i,j] = 1 when there is
   a hierarchical relationship between class i and j.

  Args:
    label_map_path_or_proto: path to StringIntLabelMap proto text file or the
      proto itself.
    include_identity: Boolean to indicate whether to include a class element
      among its ancestors and descendants. Setting this will result in the lut
      diagonal being set to 1.

  Returns:
    ancestors_lut: Look up table with the ancestors.
    descendants_lut: Look up table with the descendants.
  """
  if isinstance(label_map_path_or_proto, string_types):
    label_map = load_labelmap(label_map_path_or_proto)
  else:
    _validate_label_map(label_map_path_or_proto)
    label_map = label_map_path_or_proto

  hierarchy_dict = {
      'ancestors': collections.defaultdict(list),
      'descendants': collections.defaultdict(list)
  }
  max_id = -1
  for item in label_map.item:
    max_id = max(max_id, item.id)
    for ancestor in item.ancestor_ids:
      hierarchy_dict['ancestors'][item.id].append(ancestor)
    for descendant in item.descendant_ids:
      hierarchy_dict['descendants'][item.id].append(descendant)

  def get_graph_relations_tensor(graph_relations):
    graph_relations_tensor = np.zeros([max_id, max_id])
    for id_val, ids_related in graph_relations.items():
      id_val = int(id_val) - _LABEL_OFFSET
      for id_related in ids_related:
        id_related -= _LABEL_OFFSET
        graph_relations_tensor[id_val, id_related] = 1
    if include_identity:
      graph_relations_tensor += np.eye(max_id)
    return graph_relations_tensor

  ancestors_lut = get_graph_relations_tensor(hierarchy_dict['ancestors'])
  descendants_lut = get_graph_relations_tensor(hierarchy_dict['descendants'])
  return ancestors_lut, descendants_lut


def create_categories_from_labelmap(label_map_path, use_display_name=True):
  """Reads a label map and returns categories list compatible with eval.

  This function converts label map proto and returns a list of dicts, each of
  which  has the following keys:
    'id': an integer id uniquely identifying this category.
    'name': string representing category name e.g., 'cat', 'dog'.
    'keypoints': a dictionary of keypoint string label to integer id. It is only
      returned when available in label map proto.

  Args:
    label_map_path: Path to `StringIntLabelMap` proto text file.
    use_display_name: (boolean) choose whether to load 'display_name' field
      as category name.  If False or 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.
  """
  label_map = load_labelmap(label_map_path)
  max_num_classes = max(item.id for item in label_map.item)
  return convert_label_map_to_categories(label_map, max_num_classes,
                                         use_display_name)


def create_category_index_from_labelmap(label_map_path, use_display_name=True):
  """Reads a label map and returns a category index.

  Args:
    label_map_path: Path to `StringIntLabelMap` proto text file.
    use_display_name: (boolean) choose whether to load 'display_name' field
      as category name.  If False or if the display_name field does not exist,
      uses 'name' field as category names instead.

  Returns:
    A category index, which is a dictionary that maps integer ids to dicts
    containing categories, e.g.
    {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}, ...}
  """
  categories = create_categories_from_labelmap(label_map_path, use_display_name)
  return create_category_index(categories)


def create_class_agnostic_category_index():
  """Creates a category index with a single `object` class."""
  return {1: {'id': 1, 'name': 'object'}}
