tf.keras.ops.image.elastic_transform

Applies elastic deformation to the image(s).

tf.keras.ops.image.elastic_transform(
    images,
    alpha=20.0,
    sigma=5.0,
    interpolation='bilinear',
    fill_mode='reflect',
    fill_value=0.0,
    seed=None,
    data_format=None
)

images Input image or batch of images. Must be 3D or 4D.
alpha Scaling factor that controls the intensity of the deformation.
sigma Standard deviation of the Gaussian filter used for smoothing the displacement fields.
interpolation Interpolation method. Available methods are "nearest", and "bilinear". Defaults to "bilinear".
fill_mode Points outside the boundaries of the input are filled according to the given mode. Available methods are "constant", "nearest", "wrap" and "reflect". Defaults to "constant".

  • "reflect": (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel.
  • "constant": (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k specified by fill_value.
  • "wrap": (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge.
  • "nearest": (a a a a | a b c d | d d d d) The input is extended by the nearest pixel.
fill_value Value used for points outside the boundaries of the input if fill_mode="constant". Defaults to 0.
data_format A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, height, width). If not specified, the value will default to keras.config.image_data_format.

Transformed image or batch of images with elastic deformation.

Examples:

x = np.random.random((2, 64, 80, 3))  # batch of 2 RGB images
y = keras.ops.image.elastic_transform(x)
y.shape
(2, 64, 80, 3)
 
x = np.random.random((64, 80, 3))  # single RGB image
y = keras.ops.image.elastic_transform(x)
y.shape
(64, 80, 3)
 
x = np.random.random((2, 3, 64, 80))  # batch of 2 RGB images
y = keras.ops.image.elastic_transform(
    x, data_format="channels_first")
y.shape
(2, 3, 64, 80)