# 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.
# ==============================================================================
"""Tests for oid_vrd_challenge_evaluation_utils."""

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

import numpy as np
import pandas as pd
import tensorflow as tf
from object_detection.core import standard_fields
from object_detection.metrics import oid_vrd_challenge_evaluation_utils as utils
from object_detection.utils import vrd_evaluation


class OidVrdChallengeEvaluationUtilsTest(tf.test.TestCase):

  def testBuildGroundtruthDictionary(self):
    np_data = pd.DataFrame(
        [[
            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.3, 0.5, 0.6,
            0.0, 0.3, 0.5, 0.6, 'is', None, None
        ], [
            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/02gy9n', 0.0, 0.3, 0.5, 0.6,
            0.1, 0.2, 0.3, 0.4, 'under', None, None
        ], [
            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.1, 0.2, 0.3,
            0.0, 0.1, 0.2, 0.3, 'is', None, None
        ], [
            'fe58ec1b06db2bb7', '/m/083vt', '/m/04bcr3', 0.1, 0.2, 0.3, 0.4,
            0.5, 0.6, 0.7, 0.8, 'at', None, None
        ], [
            'fe58ec1b06db2bb7', None, None, None, None, None, None, None, None,
            None, None, None, '/m/04bcr3', 1.0
        ], [
            'fe58ec1b06db2bb7', None, None, None, None, None, None, None, None,
            None, None, None, '/m/083vt', 0.0
        ], [
            'fe58ec1b06db2bb7', None, None, None, None, None, None, None, None,
            None, None, None, '/m/02gy9n', 0.0
        ]],
        columns=[
            'ImageID', 'LabelName1', 'LabelName2', 'XMin1', 'XMax1', 'YMin1',
            'YMax1', 'XMin2', 'XMax2', 'YMin2', 'YMax2', 'RelationshipLabel',
            'LabelName', 'Confidence'
        ])
    class_label_map = {'/m/04bcr3': 1, '/m/083vt': 2, '/m/02gy9n': 3}
    relationship_label_map = {'is': 1, 'under': 2, 'at': 3}
    groundtruth_dictionary = utils.build_groundtruth_vrd_dictionary(
        np_data, class_label_map, relationship_label_map)

    self.assertTrue(standard_fields.InputDataFields.groundtruth_boxes in
                    groundtruth_dictionary)
    self.assertTrue(standard_fields.InputDataFields.groundtruth_classes in
                    groundtruth_dictionary)
    self.assertTrue(standard_fields.InputDataFields.groundtruth_image_classes in
                    groundtruth_dictionary)

    self.assertAllEqual(
        np.array(
            [(1, 2, 1), (1, 3, 2), (1, 2, 1), (2, 1, 3)],
            dtype=vrd_evaluation.label_data_type), groundtruth_dictionary[
                standard_fields.InputDataFields.groundtruth_classes])
    expected_vrd_data = np.array(
        [
            ([0.5, 0.0, 0.6, 0.3], [0.5, 0.0, 0.6, 0.3]),
            ([0.5, 0.0, 0.6, 0.3], [0.3, 0.1, 0.4, 0.2]),
            ([0.2, 0.0, 0.3, 0.1], [0.2, 0.0, 0.3, 0.1]),
            ([0.3, 0.1, 0.4, 0.2], [0.7, 0.5, 0.8, 0.6]),
        ],
        dtype=vrd_evaluation.vrd_box_data_type)
    for field in expected_vrd_data.dtype.fields:
      self.assertNDArrayNear(
          expected_vrd_data[field], groundtruth_dictionary[
              standard_fields.InputDataFields.groundtruth_boxes][field], 1e-5)
    self.assertAllEqual(
        np.array([1, 2, 3]), groundtruth_dictionary[
            standard_fields.InputDataFields.groundtruth_image_classes])

  def testBuildPredictionDictionary(self):
    np_data = pd.DataFrame(
        [[
            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.3, 0.5, 0.6,
            0.0, 0.3, 0.5, 0.6, 'is', 0.1
        ], [
            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/02gy9n', 0.0, 0.3, 0.5, 0.6,
            0.1, 0.2, 0.3, 0.4, 'under', 0.2
        ], [
            'fe58ec1b06db2bb7', '/m/04bcr3', '/m/083vt', 0.0, 0.1, 0.2, 0.3,
            0.0, 0.1, 0.2, 0.3, 'is', 0.3
        ], [
            'fe58ec1b06db2bb7', '/m/083vt', '/m/04bcr3', 0.1, 0.2, 0.3, 0.4,
            0.5, 0.6, 0.7, 0.8, 'at', 0.4
        ]],
        columns=[
            'ImageID', 'LabelName1', 'LabelName2', 'XMin1', 'XMax1', 'YMin1',
            'YMax1', 'XMin2', 'XMax2', 'YMin2', 'YMax2', 'RelationshipLabel',
            'Score'
        ])
    class_label_map = {'/m/04bcr3': 1, '/m/083vt': 2, '/m/02gy9n': 3}
    relationship_label_map = {'is': 1, 'under': 2, 'at': 3}
    prediction_dictionary = utils.build_predictions_vrd_dictionary(
        np_data, class_label_map, relationship_label_map)

    self.assertTrue(standard_fields.DetectionResultFields.detection_boxes in
                    prediction_dictionary)
    self.assertTrue(standard_fields.DetectionResultFields.detection_classes in
                    prediction_dictionary)
    self.assertTrue(standard_fields.DetectionResultFields.detection_scores in
                    prediction_dictionary)

    self.assertAllEqual(
        np.array(
            [(1, 2, 1), (1, 3, 2), (1, 2, 1), (2, 1, 3)],
            dtype=vrd_evaluation.label_data_type), prediction_dictionary[
                standard_fields.DetectionResultFields.detection_classes])
    expected_vrd_data = np.array(
        [
            ([0.5, 0.0, 0.6, 0.3], [0.5, 0.0, 0.6, 0.3]),
            ([0.5, 0.0, 0.6, 0.3], [0.3, 0.1, 0.4, 0.2]),
            ([0.2, 0.0, 0.3, 0.1], [0.2, 0.0, 0.3, 0.1]),
            ([0.3, 0.1, 0.4, 0.2], [0.7, 0.5, 0.8, 0.6]),
        ],
        dtype=vrd_evaluation.vrd_box_data_type)
    for field in expected_vrd_data.dtype.fields:
      self.assertNDArrayNear(
          expected_vrd_data[field], prediction_dictionary[
              standard_fields.DetectionResultFields.detection_boxes][field],
          1e-5)
    self.assertNDArrayNear(
        np.array([0.1, 0.2, 0.3, 0.4]), prediction_dictionary[
            standard_fields.DetectionResultFields.detection_scores], 1e-5)


if __name__ == '__main__':
  tf.test.main()
