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          (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’). Default is ‘constant’

          order - The order of the spline interpolation, default is 3. The order has to be in the range 0-5.

          """

    if len(self.Image.shape) == 2:

      return scipy.ndimage.shift(

          self.Image, [height, width], order=order, mode=mode)

    if len(self.Image.shape == 3):

      return scipy.ndimage.shift(

          self.Image, [height, width, 0], order=order, mode=mode)

  def zoom(self, zoom_x=1, zoom_y=1, mode='constant', order=3):

    """Shifts the image

        Parameters:

          zoom_x - amount of zoom along x axis

          zoom_y - amount of zoom along y axis

          mode - Points outside the boundaries of the input are filled according to the given mode

          (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’). Default is ‘constant’.

          order - The order of the spline interpolation, default is 3. The order has to be in the range 0-5.

          """

    h, w = self.Image.shape[0], self.Image.shape[1]

    o_x = float(h) / 2 + 0.5

    o_y = float(w) / 2 + 0.5

    transform_matrix = np.array([[zoom_x, 0, 0], [0, zoom_y, 0], [0, 0, 1]])

    offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])

    reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])

    transform_matrix = np.dot(

        np.dot(offset_matrix, transform_matrix), reset_matrix)

    final_affine_matrix = np.linalg.inv(transform_matrix[:2, :2])

    final_offset = transform_matrix[:2, 2]

    x = np.rollaxis(self.Image, 2, 0)

    channel_images = [

        scipy.ndimage.interpolation.affine_transform(

            x_channel,

            final_affine_matrix,

            final_offset,

            order=order,

            mode=mode) for x_channel in x

    ]

    x = np.stack(channel_images, axis=0)

    x = np.rollaxis(x, 0, 3)

    return x

  def gaussian_noise(self):

    '''Adds gaussian noise to the image

        '''