text: # Copyright 2019 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 DELF feature extractor."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl.testing import parameterized
  import numpy as np
  import tensorflow as tf
  
  from delf import delf_config_pb2
  from delf import extractor
  
  
  class ExtractorTest(tf.test.TestCase, parameterized.TestCase):
  
    @parameterized.named_parameters(
        ('Max-1Min-1', -1, -1, 1.0, False, [4, 2, 3], [1.0, 1.0]),
        ('Max-1Min-1Square', -1, -1, 1.0, True, [4, 4, 3], [1.0, 2.0]),
        ('Max2Min-1', 2, -1, 1.0, False, [2, 1, 3], [0.5, 0.5]),
        ('Max2Min-1Square', 2, -1, 1.0, True, [2, 2, 3], [0.5, 1.0]),
        ('Max8Min-1', 8, -1, 1.0, False, [4, 2, 3], [1.0, 1.0]),
        ('Max8Min-1Square', 8, -1, 1.0, True, [4, 4, 3], [1.0, 2.0]),
        ('Max-1Min1', -1, 1, 1.0, False, [4, 2, 3], [1.0, 1.0]),
        ('Max-1Min1Square', -1, 1, 1.0, True, [4, 4, 3], [1.0, 2.0]),
        ('Max-1Min8', -1, 8, 1.0, False, [8, 4, 3], [2.0, 2.0]),
        ('Max-1Min8Square', -1, 8, 1.0, True, [8, 8, 3], [2.0, 4.0]),
        ('Max16Min8', 16, 8, 1.0, False, [8, 4, 3], [2.0, 2.0]),
        ('Max16Min8Square', 16, 8, 1.0, True, [8, 8, 3], [2.0, 4.0]),
        ('Max2Min2', 2, 2, 1.0, False, [2, 1, 3], [0.5, 0.5]),
        ('Max2Min2Square', 2, 2, 1.0, True, [2, 2, 3], [0.5, 1.0]),
        ('Max-1Min-1Factor0.5', -1, -1, 0.5, False, [4, 2, 3], [1.0, 1.0]),
        ('Max-1Min-1Factor0.5Square', -1, -1, 0.5, True, [4, 4, 3], [1.0, 2.0]),
        ('Max2Min-1Factor2.0', 2, -1, 2.0, False, [4, 2, 3], [1.0, 1.0]),
        ('Max2Min-1Factor2.0Square', 2, -1, 2.0, True, [4, 4, 3], [1.0, 2.0]),
        ('Max-1Min8Factor0.5', -1, 8, 0.5, False, [4, 2, 3], [1.0, 1.0]),
        ('Max-1Min8Factor0.5Square', -1, 8, 0.5, True, [4, 4, 3], [1.0, 2.0]),
        ('Max-1Min8Factor0.25', -1, 8, 0.25, False, [4, 2, 3], [1.0, 1.0]),
        ('Max-1Min8Factor0.25Square', -1, 8, 0.25, True, [4, 4, 3], [1.0, 2.0]),
        ('Max2Min2Factor2.0', 2, 2, 2.0, False, [4, 2, 3], [1.0, 1.0]),
        ('Max2Min2Factor2.0Square', 2, 2, 2.0, True, [4, 4, 3], [1.0, 2.0]),
        ('Max16Min8Factor0.5', 16, 8, 0.5, False, [4, 2, 3], [1.0, 1.0]),
        ('Max16Min8Factor0.5Square', 16, 8, 0.5, True, [4, 4, 3], [1.0, 2.0]),
    )
    def testResizeImageWorks(self, max_image_size, min_image_size, resize_factor,
                             square_output, expected_shape,
                             expected_scale_factors):
      # Construct image of size 4x2x3.
      image = np.array([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [3, 3, 3]],
                        [[4, 4, 4], [5, 5, 5]], [[6, 6, 6], [7, 7, 7]]],
                       dtype='uint8')
  
      # Set up config.
      config = delf_config_pb2.DelfConfig(
          max_image_size=max_image_size, min_image_size=min_image_size)
  
      resized_image, scale_factors = extractor.ResizeImage(
          image, config, resize_factor, square_output)
      self.assertAllEqual(resized_image.shape, expected_shape)
      self.assertAllClose(scale_factors, expected_scale_factors)
  
    @parameterized.named_parameters(
        ('Max2Min2', 2, 2, 1.0, False, [2, 1, 3], [0.666666, 0.5]),
        ('Max2Min2Square', 2, 2, 1.0, True, [2, 2, 3], [0.666666, 1.0]),
    )
    def testResizeImageRoundingWorks(self, max_image_size, min_image_size,
                                     resize_factor, square_output, expected_shape,
                                     expected_scale_factors):
      # Construct image of size 3x2x3.
      image = np.array([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [3, 3, 3]],
                        [[4, 4, 4], [5, 5, 5]]],
                       dtype='uint8')
  
      # Set up config.
      config = delf_config_pb2.DelfConfig(
          max_image_size=max_image_size, min_image_size=min_image_size)
  
      resized_image, scale_factors = extractor.ResizeImage(
          image, config, resize_factor, square_output)
      self.assertAllEqual(resized_image.shape, expected_shape)
      self.assertAllClose(scale_factors, expected_scale_factors)
  
  
  if __name__ == '__main__':
    tf.test.main()
  