text: # 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
  # limitations under the License.
  # ==============================================================================
  """Tests for object_detection.data_decoders.tf_example_parser."""
  
  import numpy as np
  import numpy.testing as np_testing
  import tensorflow as tf
  
  from object_detection.core import standard_fields as fields
  from object_detection.metrics import tf_example_parser
  
  
  class TfExampleDecoderTest(tf.test.TestCase):
  
    def _Int64Feature(self, value):
      return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
  
    def _FloatFeature(self, value):
      return tf.train.Feature(float_list=tf.train.FloatList(value=value))
  
    def _BytesFeature(self, value):
      return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
  
    def testParseDetectionsAndGT(self):
      source_id = 'abc.jpg'
      # y_min, x_min, y_max, x_max
      object_bb = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6], [1.0, 0.6, 0.8],
                            [1.0, 0.6, 0.7]]).transpose()
      detection_bb = np.array([[0.1, 0.2], [0.0, 0.8], [1.0, 0.6],
                               [1.0, 0.85]]).transpose()
  
      object_class_label = [1, 1, 2]
      object_difficult = [1, 0, 0]
      object_group_of = [0, 0, 1]
      verified_labels = [1, 2, 3, 4]
      detection_class_label = [2, 1]
      detection_score = [0.5, 0.3]
      features = {
          fields.TfExampleFields.source_id:
              self._BytesFeature(source_id),
          fields.TfExampleFields.object_bbox_ymin:
              self._FloatFeature(object_bb[:, 0].tolist()),
          fields.TfExampleFields.object_bbox_xmin:
              self._FloatFeature(object_bb[:, 1].tolist()),
          fields.TfExampleFields.object_bbox_ymax:
              self._FloatFeature(object_bb[:, 2].tolist()),
          fields.TfExampleFields.object_bbox_xmax:
              self._FloatFeature(object_bb[:, 3].tolist()),
          fields.TfExampleFields.detection_bbox_ymin:
              self._FloatFeature(detection_bb[:, 0].tolist()),
          fields.TfExampleFields.detection_bbox_xmin:
              self._FloatFeature(detection_bb[:, 1].tolist()),
          fields.TfExampleFields.detection_bbox_ymax:
              self._FloatFeature(detection_bb[:, 2].tolist()),
          fields.TfExampleFields.detection_bbox_xmax:
              self._FloatFeature(detection_bb[:, 3].tolist()),
          fields.TfExampleFields.detection_class_label:
              self._Int64Feature(detection_class_label),
          fields.TfExampleFields.detection_score:
              self._FloatFeature(detection_score),
      }
  
      example = tf.train.Example(features=tf.train.Features(feature=features))
      parser = tf_example_parser.TfExampleDetectionAndGTParser()
  
      results_dict = parser.parse(example)
      self.assertIsNone(results_dict)
  
      features[fields.TfExampleFields.object_class_label] = (
          self._Int64Feature(object_class_label))
      features[fields.TfExampleFields.object_difficult] = (
          self._Int64Feature(object_difficult))
  
      example = tf.train.Example(features=tf.train.Features(feature=features))
      results_dict = parser.parse(example)
  
      self.assertIsNotNone(results_dict)
      self.assertEqual(source_id, results_dict[fields.DetectionResultFields.key])
      np_testing.assert_almost_equal(
          object_bb, results_dict[fields.InputDataFields.groundtruth_boxes])
      np_testing.assert_almost_equal(
          detection_bb,
          results_dict[fields.DetectionResultFields.detection_boxes])
      np_testing.assert_almost_equal(
          detection_score,
          results_dict[fields.DetectionResultFields.detection_scores])
      np_testing.assert_almost_equal(
          detection_class_label,
          results_dict[fields.DetectionResultFields.detection_classes])
      np_testing.assert_almost_equal(
          object_difficult,
          results_dict[fields.InputDataFields.groundtruth_difficult])
      np_testing.assert_almost_equal(
          object_class_label,
          results_dict[fields.InputDataFields.groundtruth_classes])
  
      parser = tf_example_parser.TfExampleDetectionAndGTParser()
  
      features[fields.TfExampleFields.object_group_of] = (
          self._Int64Feature(object_group_of))
  
      example = tf.train.Example(features=tf.train.Features(feature=features))
      results_dict = parser.parse(example)
      self.assertIsNotNone(results_dict)
      np_testing.assert_equal(
          object_group_of,
          results_dict[fields.InputDataFields.groundtruth_group_of])
  
      features[fields.TfExampleFields.image_class_label] = (
          self._Int64Feature(verified_labels))
  
      example = tf.train.Example(features=tf.train.Features(feature=features))
      results_dict = parser.parse(example)
      self.assertIsNotNone(results_dict)
      np_testing.assert_equal(
          verified_labels, results_dict[fields.InputDataFields.verified_labels])
  
    def testParseString(self):
      string_val = 'abc'
      features = {'string': self._BytesFeature(string_val)}
      example = tf.train.Example(features=tf.train.Features(feature=features))
  
      parser = tf_example_parser.StringParser('string')
      result = parser.parse(example)
      self.assertIsNotNone(result)
      self.assertEqual(result, string_val)
  
      parser = tf_example_parser.StringParser('another_string')
      result = parser.parse(example)
      self.assertIsNone(result)
  
    def testParseFloat(self):
      float_array_val = [1.5, 1.4, 2.0]
      features = {'floats': self._FloatFeature(float_array_val)}
      example = tf.train.Example(features=tf.train.Features(feature=features))
  
      parser = tf_example_parser.FloatParser('floats')
      result = parser.parse(example)
      self.assertIsNotNone(result)
      np_testing.assert_almost_equal(result, float_array_val)
  
      parser = tf_example_parser.StringParser('another_floats')
      result = parser.parse(example)
      self.assertIsNone(result)
  
    def testInt64Parser(self):
      int_val = [1, 2, 3]
      features = {'ints': self._Int64Feature(int_val)}
      example = tf.train.Example(features=tf.train.Features(feature=features))
  
      parser = tf_example_parser.Int64Parser('ints')
      result = parser.parse(example)
      self.assertIsNotNone(result)
      np_testing.assert_almost_equal(result, int_val)
  
      parser = tf_example_parser.Int64Parser('another_ints')
      result = parser.parse(example)
      self.assertIsNone(result)
  
    def testBoundingBoxParser(self):
      bounding_boxes = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6],
                                 [1.0, 0.6, 0.8], [1.0, 0.6, 0.7]]).transpose()
      features = {
          'ymin': self._FloatFeature(bounding_boxes[:, 0]),
          'xmin': self._FloatFeature(bounding_boxes[:, 1]),
          'ymax': self._FloatFeature(bounding_boxes[:, 2]),
          'xmax': self._FloatFeature(bounding_boxes[:, 3])
      }
  
      example = tf.train.Example(features=tf.train.Features(feature=features))
  
      parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax', 'ymax')
      result = parser.parse(example)
      self.assertIsNotNone(result)
      np_testing.assert_almost_equal(result, bounding_boxes)
  
      parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax',
                                                   'another_ymax')
      result = parser.parse(example)
      self.assertIsNone(result)
  
  
  if __name__ == '__main__':
    tf.test.main()
  