class menpo.shape.base.Shape[source]

Bases: Vectorizable, Transformable, Landmarkable, LandmarkableViewable, Viewable

Abstract representation of shape. Shapes are Transformable, Vectorizable, Landmarkable, LandmarkableViewable and Viewable. This base class handles transforming landmarks when the shape is transformed. Therefore, implementations of Shape have to implement the abstract _transform_self_inplace() method that handles transforming the Shape itself.


Returns a flattened representation of the object as a single vector.

Returns:vector ((N,) ndarray) – The core representation of the object, flattened into a single vector. Note that this is always a view back on to the original object, but is not writable.

Generate an efficient copy of this object.

Note that Numpy arrays and other Copyable objects on self will be deeply copied. Dictionaries and sets will be shallow copied, and everything else will be assigned (no copy will be made).

Classes that store state other than numpy arrays and immutable types should overwrite this method to ensure all state is copied.

Returns:type(self) – A copy of this object

Build a new instance of the object from it’s vectorized state.

self is used to fill out the missing state required to rebuild a full object from it’s standardized flattened state. This is the default implementation, which is which is a deepcopy of the object followed by a call to from_vector_inplace(). This method can be overridden for a performance benefit if desired.

Parameters:vector ((n_parameters,) ndarray) – Flattened representation of the object.
Returns:object (type(self)) – An new instance of this class.

Deprecated. Use the non-mutating API, from_vector.

For internal usage in performance-sensitive spots, see _from_vector_inplace()

Parameters:vector ((n_parameters,) ndarray) – Flattened representation of this object

Tests if the vectorized form of the object contains nan values or not. This is particularly useful for objects with unknown values that have been mapped to nan values.

Returns:has_nan_values (bool) – If the vectorized object contains nan values.

The total number of dimensions.


Whether the object has landmarks.


The landmarks object.


The number of landmark groups on this object.


The length of the vector that this object produces.