text: # 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.
  # ==============================================================================
  r"""Runs evaluation using OpenImages groundtruth and predictions.
  
  Example usage:
  python \
  models/research/object_detection/metrics/oid_vrd_challenge_evaluation.py \
      --input_annotations_boxes=/path/to/input/annotations-human-bbox.csv \
      --input_annotations_labels=/path/to/input/annotations-label.csv \
      --input_class_labelmap=/path/to/input/class_labelmap.pbtxt \
      --input_relationship_labelmap=/path/to/input/relationship_labelmap.pbtxt \
      --input_predictions=/path/to/input/predictions.csv \
      --output_metrics=/path/to/output/metric.csv \
  
  CSVs with bounding box annotations and image label (including the image URLs)
  can be downloaded from the Open Images Challenge website:
  https://storage.googleapis.com/openimages/web/challenge.html
  The format of the input csv and the metrics itself are described on the
  challenge website.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import pandas as pd
  from google.protobuf import text_format
  
  from research.object_detection.metrics import io_utils
  from research.object_detection.metrics import oid_vrd_challenge_evaluation_utils as utils
  from research.object_detection.protos import string_int_label_map_pb2
  from research.object_detection.utils import vrd_evaluation
  
  
  def _load_labelmap(labelmap_path):
      """Loads labelmap from the labelmap path.
  
    Args:
      labelmap_path: Path to the labelmap.
  
    Returns:
      A dictionary mapping class name to class numerical id.
    """
  
      label_map = string_int_label_map_pb2.StringIntLabelMap()
      with open(labelmap_path, 'r') as fid:
          label_map_string = fid.read()
          text_format.Merge(label_map_string, label_map)
      labelmap_dict = {}
      for item in label_map.item:
          labelmap_dict[item.name] = item.id
      return labelmap_dict
  
  
  def _swap_labelmap_dict(labelmap_dict):
      """Swaps keys and labels in labelmap.
  
    Args:
      labelmap_dict: Input dictionary.
  
    Returns:
      A dictionary mapping class name to class numerical id.
    """
      return dict((labelmap_dict.get(k), k) for k in labelmap_dict)
  
  
  def main(parsed_args):
      all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
      all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
      all_annotations = pd.concat([all_box_annotations, all_label_annotations])
      class_label_map = _load_labelmap(parsed_args.input_class_labelmap)
      relationship_label_map = _load_labelmap(
          parsed_args.input_relationship_labelmap)
  
      relation_evaluator = vrd_evaluation.VRDRelationDetectionEvaluator()
      phrase_evaluator = vrd_evaluation.VRDPhraseDetectionEvaluator()
  
      for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
          image_id, image_groundtruth = groundtruth
          groundtruth_dictionary = utils.build_groundtruth_vrd_dictionary(
              image_groundtruth, class_label_map, relationship_label_map)
  
          relation_evaluator.add_single_ground_truth_image_info(
              image_id, groundtruth_dictionary)
          phrase_evaluator.add_single_ground_truth_image_info(image_id,
                                                              groundtruth_dictionary)
  
      all_predictions = pd.read_csv(parsed_args.input_predictions)
      for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
          image_id, image_predictions = prediction_data
          prediction_dictionary = utils.build_predictions_vrd_dictionary(
              image_predictions, class_label_map, relationship_label_map)
  
          relation_evaluator.add_single_detected_image_info(image_id,
                                                            prediction_dictionary)
          phrase_evaluator.add_single_detected_image_info(image_id,
                                                          prediction_dictionary)
  
      relation_metrics = relation_evaluator.evaluate(
          relationships=_swap_labelmap_dict(relationship_label_map))
      phrase_metrics = phrase_evaluator.evaluate(
          relationships=_swap_labelmap_dict(relationship_label_map))
  
      with open(parsed_args.output_metrics, 'w') as fid:
          io_utils.write_csv(fid, relation_metrics)
          io_utils.write_csv(fid, phrase_metrics)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(
          description='Evaluate Open Images Visual Relationship Detection predictions.')
      parser.add_argument(
          '--input_annotations_boxes',
          required=True,
          help='File with groundtruth vrd annotations.')
      parser.add_argument(
          '--input_annotations_labels',
          required=True,
          help='File with groundtruth labels annotations')
      parser.add_argument(
          '--input_predictions',
          required=True,
          help="""File with detection predictions; NOTE: no postprocessing is
        applied in the evaluation script.""")
      parser.add_argument(
          '--input_class_labelmap',
          required=True,
          help="""OpenImages Challenge labelmap; note: it is expected to include
        attributes.""")
      parser.add_argument(
          '--input_relationship_labelmap',
          required=True,
          help="""OpenImages Challenge relationship labelmap.""")
      parser.add_argument(
          '--output_metrics', required=True, help='Output file with csv metrics')
  
      args = parser.parse_args()
      main(args)
  