text: """
  Copyright 2017-2018 Fizyr (https://fizyr.com)
  
  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.
  """
  
  import keras.backend
  from keras.preprocessing.image import ImageDataGenerator
  from keras_retinanet.preprocessing.generator import Generator
  
  import numpy as np
  import pytest
  
  
  class SimpleGenerator(Generator):
      def __init__(self, annotations_group, num_classes=0, image=None):
          self.annotations_group = annotations_group
          self.num_classes_      = num_classes
          self.image             = image
          super(SimpleGenerator, self).__init__(ImageDataGenerator(), group_method='none', shuffle_groups=False)
  
      def num_classes(self):
          return self.num_classes_
  
      def load_image(self, image_index):
          return self.image
  
      def size(self):
          return len(self.annotations_group)
  
      def load_annotations(self, image_index):
          result = self.annotations_group[image_index]
          return result
  
  
  class TestLoadAnnotationsGroup(object):
      def test_simple(self):
          input_annotations_group = [
              np.array([
                  [  0,   0,  10,  10],
                  [150, 150, 350, 350]
              ]),
          ]
          expected_annotations_group = input_annotations_group
  
          simple_generator = SimpleGenerator(input_annotations_group)
          annotations_group = simple_generator.load_annotations_group(simple_generator.groups[0])
  
          np.testing.assert_equal(expected_annotations_group, annotations_group)
  
      def test_multiple(self):
          input_annotations_group = [
              np.array([
                  [  0,   0,  10,  10],
                  [150, 150, 350, 350]
              ]),
              np.array([
                  [0, 0, 1, 1]
              ])
          ]
          expected_annotations_group = input_annotations_group
  
          simple_generator = SimpleGenerator(input_annotations_group)
          annotations_group_0 = simple_generator.load_annotations_group(simple_generator.groups[0])
          annotations_group_1 = simple_generator.load_annotations_group(simple_generator.groups[1])
  
          np.testing.assert_equal([expected_annotations_group[0]], annotations_group_0)
          np.testing.assert_equal([expected_annotations_group[1]], annotations_group_1)
  
  
  class TestFilterAnnotations(object):
      def test_simple_filter(self):
          input_annotations_group = [
              np.array([
                  [  0,   0, 10, 10],
                  [150, 150, 50, 50]
              ]),
          ]
  
          input_image = np.zeros((500, 500, 3))
  
          expected_annotations_group = [
              np.array([
                  [0, 0, 10, 10],
              ]),
          ]
  
          simple_generator = SimpleGenerator(input_annotations_group)
          annotations_group = simple_generator.load_annotations_group(simple_generator.groups[0])
          # expect a UserWarning
          with pytest.warns(UserWarning):
              image_group, annotations_group = simple_generator.filter_annotations([input_image], annotations_group, simple_generator.groups[0])
  
          np.testing.assert_equal(expected_annotations_group, annotations_group)
  
      def test_multiple_filter(self):
          input_annotations_group = [
              np.array([
                  [  0,   0,  10,  10],
                  [150, 150,  50,  50],
                  [150, 150, 350, 350],
                  [350, 350, 150, 150],
                  [  1,   1,   2,   2],
                  [  2,   2,   1,   1]
              ]),
              np.array([
                  [0, 0, -1, -1]
              ]),
              np.array([
                  [-10, -10,    0,    0],
                  [-10, -10, -100, -100],
                  [ 10,  10,  100,  100]
              ]),
              np.array([
                  [ 10,  10,  100,  100],
                  [ 10,  10,  600,  600]
              ]),
          ]
  
          input_image = np.zeros((500, 500, 3))
  
          expected_annotations_group = [
              np.array([
                  [  0,   0,  10,  10],
                  [150, 150, 350, 350],
                  [  1,   1,   2,   2]
              ]),
              np.zeros((0, 4)),
              np.array([
                  [10, 10, 100, 100]
              ]),
              np.array([
                  [ 10,  10,  100,  100]
              ]),
          ]
  
          simple_generator = SimpleGenerator(input_annotations_group)
          # expect a UserWarning
          annotations_group_0 = simple_generator.load_annotations_group(simple_generator.groups[0])
          with pytest.warns(UserWarning):
              image_group, annotations_group_0 = simple_generator.filter_annotations([input_image], annotations_group_0, simple_generator.groups[0])
  
          annotations_group_1 = simple_generator.load_annotations_group(simple_generator.groups[1])
          with pytest.warns(UserWarning):
              image_group, annotations_group_1 = simple_generator.filter_annotations([input_image], annotations_group_1, simple_generator.groups[1])
  
          annotations_group_2 = simple_generator.load_annotations_group(simple_generator.groups[2])
          with pytest.warns(UserWarning):
              image_group, annotations_group_2 = simple_generator.filter_annotations([input_image], annotations_group_2, simple_generator.groups[2])
  
          np.testing.assert_equal([expected_annotations_group[0]], annotations_group_0)
          np.testing.assert_equal([expected_annotations_group[1]], annotations_group_1)
          np.testing.assert_equal([expected_annotations_group[2]], annotations_group_2)
  
      def test_complete(self):
          input_annotations_group = [
              np.array([
                  [  0,   0, 50, 50, 0],  # one object of class 0
                  [150, 150, 50, 50, 1],  # one object of class 1 with an invalid box
              ], dtype=keras.backend.floatx()),
          ]
  
          input_image = np.zeros((500, 500, 3), dtype=np.uint8)
  
          expected_annotations_group = [
              np.array([
                  [0, 0, 10, 10],
              ]),
          ]
  
          simple_generator = SimpleGenerator(input_annotations_group, image=input_image, num_classes=2)
          # expect a UserWarning
          with pytest.warns(UserWarning):
              _, [_, labels_batch] = simple_generator.next()
  
          # test that only object with class 0 is present in labels_batch
          labels = np.unique(np.argmax(labels_batch == 1, axis=2))
          assert(len(labels) == 1 and labels[0] == 0), 'Expected only class 0 to be present, but got classes {}'.format(labels)
  