text: import unittest
  import numpy as np
  from skimage.util.shape import view_as_windows
  
  from skater.util.image_ops import view_windows
  from skater.util.image_ops import load_image
  from skater.util.image_ops import standard_scaler
  from skater.util.image_ops import normalize
  from skater.util.image_ops import flip_orientation
  
  
  class TestImageOps(unittest.TestCase):
  
      def setUp(self):
          pass
  
  
      def test_loading_imgs(self):
          img = load_image('skater/tests/data/pandas.jpg', 299, 299)
          self.assertEquals(img.shape, (299, 299, 3))
          img = load_image('skater/tests/data/pandas.jpg', 200, 299)
          self.assertEquals(img.shape, (200, 299, 3))
  
  
      def test_view_windows(self):
          input_matrix = np.arange(10 * 10).reshape(10, 10)
          padded_input = np.pad(input_matrix, ((0, 2), (0, 2)), mode='constant', constant_values=0)
          input_view = view_as_windows(padded_input, (4, 4), 4)
          # apply rolling window with padding to handle corner cases
          input_view_modified = view_windows(padded_input, (4, 4), 4)
          self.assertEquals(input_view.shape, input_view_modified.shape)
  
  
      def test_standard_scaler(self):
          x = np.asarray([1, 4, 5, 6, 6, 2, 3])
          x_new = standard_scaler(x)
          self.assertEquals(round(x_new.std(), 1), 1)
  
  
      def test_normalization(self):
          x = np.asarray([1, 4, 5, 6, 6, 2, 3])
          x_new = normalize(x)
          self.assertEquals((np.min(x_new), np.max(x_new)), (0, 1))
  
  
      def test_image_ops(self):
          # This is not an extensive test, but at-least a start
          X = np.arange(8).reshape((2, 2, 2))
          original_value = X[0, 0, 0]
          post_horizontal_flip = flip_orientation(X)
          self.assertEquals(original_value != post_horizontal_flip[0, 0, 0], True)
  
  
  if __name__ == '__main__':
      suite = unittest.TestLoader().loadTestsFromTestCase(TestImageOps)
      unittest.TextTestRunner(verbosity=2).run(suite)
  