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.utils.test_utils."""
  
  import numpy as np
  import tensorflow as tf
  
  from object_detection.utils import test_utils
  
  
  class TestUtilsTest(tf.test.TestCase):
  
    def test_diagonal_gradient_image(self):
      """Tests if a good pyramid image is created."""
      pyramid_image = test_utils.create_diagonal_gradient_image(3, 4, 2)
  
      # Test which is easy to understand.
      expected_first_channel = np.array([[3, 2, 1, 0],
                                         [4, 3, 2, 1],
                                         [5, 4, 3, 2]], dtype=np.float32)
      self.assertAllEqual(np.squeeze(pyramid_image[:, :, 0]),
                          expected_first_channel)
  
      # Actual test.
      expected_image = np.array([[[3, 30],
                                  [2, 20],
                                  [1, 10],
                                  [0, 0]],
                                 [[4, 40],
                                  [3, 30],
                                  [2, 20],
                                  [1, 10]],
                                 [[5, 50],
                                  [4, 40],
                                  [3, 30],
                                  [2, 20]]], dtype=np.float32)
  
      self.assertAllEqual(pyramid_image, expected_image)
  
    def test_random_boxes(self):
      """Tests if valid random boxes are created."""
      num_boxes = 1000
      max_height = 3
      max_width = 5
      boxes = test_utils.create_random_boxes(num_boxes,
                                             max_height,
                                             max_width)
  
      true_column = np.ones(shape=(num_boxes)) == 1
      self.assertAllEqual(boxes[:, 0] < boxes[:, 2], true_column)
      self.assertAllEqual(boxes[:, 1] < boxes[:, 3], true_column)
  
      self.assertTrue(boxes[:, 0].min() >= 0)
      self.assertTrue(boxes[:, 1].min() >= 0)
      self.assertTrue(boxes[:, 2].max() <= max_height)
      self.assertTrue(boxes[:, 3].max() <= max_width)
  
  
  if __name__ == '__main__':
    tf.test.main()
  