Translation¶

class menpo.transform.Translation(translation, skip_checks=False)[source]¶

Bases: DiscreteAffine, Similarity

An N-dimensional translation transform.

Parameters:	translation ((D,) ndarray) – The translation in each axis.

apply(x, **kwargs)¶

Applies this transform to x.

If x is Transformable, x will be handed this transform object to transform itself non-destructively (a transformed copy of the object will be returned).

If not, x is assumed to be an ndarray. The transformation will be non-destructive, returning the transformed version.

Any kwargs will be passed to the specific transform _apply() method.

Parameters:	x (`Transformable` or `(n_points, n_dims)` ndarray) – The array or object to be transformed. kwargs (dict) – Passed through to `_apply()`.
Returns:	transformed (`type(x)`) – The transformed object or array

apply_inplace(x, **kwargs)¶

Applies this transform to a Transformable x destructively.

Any kwargs will be passed to the specific transform _apply() method.

Parameters:	x (`Transformable`) – The `Transformable` object to be transformed. kwargs (dict) – Passed through to `_apply()`.
Returns:	transformed (`type(x)`) – The transformed object

as_vector(**kwargs)¶

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.

compose_after(transform)¶

A Transform that represents this transform composed after the given transform:

c = a.compose_after(b)
c.apply(p) == a.apply(b.apply(p))

a and b are left unchanged.

This corresponds to the usual mathematical formalism for the compose operator, o.

An attempt is made to perform native composition, but will fall back to a TransformChain as a last resort. See composes_with for a description of how the mode of composition is decided.

Parameters:	transform (`Transform` or `TransformChain`) – Transform to be applied before `self` Returns – -------- – transform – If the composition was native, a single new `Transform` will be returned. If not, a `TransformChain` is returned instead.

compose_after_inplace(transform)¶

Update self so that it represents this transform composed after the given transform:

a_orig = a.copy()
a.compose_after_inplace(b)
a.apply(p) == a_orig.apply(b.apply(p))

a is permanently altered to be the result of the composition. b is left unchanged.

Parameters:	transform (`composes_inplace_with`) – Transform to be applied before `self`
Raises:	`ValueError` – If `transform` isn’t an instance of `composes_inplace_with`

compose_before(transform)¶

A Transform that represents this transform composed before the given transform:

c = a.compose_before(b)
c.apply(p) == b.apply(a.apply(p))

a and b are left unchanged.

An attempt is made to perform native composition, but will fall back to a TransformChain as a last resort. See composes_with for a description of how the mode of composition is decided.

Parameters:	transform (`Transform` or `TransformChain`) – Transform to be applied after `self` Returns – -------- – transform – If the composition was native, a single new `Transform` will be returned. If not, a `TransformChain` is returned instead.

compose_before_inplace(transform)¶

Update self so that it represents this transform composed before the given transform:

a_orig = a.copy()
a.compose_before_inplace(b)
a.apply(p) == b.apply(a_orig.apply(p))

a is permanently altered to be the result of the composition. b is left unchanged.

Parameters:	transform (`composes_inplace_with`) – Transform to be applied after `self`
Raises:	`ValueError` – If `transform` isn’t an instance of `composes_inplace_with`

copy()¶

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

decompose()¶

A DiscreteAffine is already maximally decomposed - return a copy of self in a list.

Returns:	transform (`DiscreteAffine`) – Deep copy of self.

from_vector(vector)¶

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:	transform (`type(self)`) – An new instance of this class.

pseudoinverse()[source]¶

The inverse translation (negated).

Returns:	`Translation`

pseudoinverse_vector(vector)¶

The vectorized pseudoinverse of a provided vector instance. Syntactic sugar for:

self.from_vector(vector).pseudoinverse().as_vector()

Can be much faster than the explict call as object creation can be entirely avoided in some cases.

Parameters:	vector (`(n_parameters,)` ndarray) – A vectorized version of `self`
Returns:	pseudoinverse_vector (`(n_parameters,)` ndarray) – The pseudoinverse of the vector provided

set_h_matrix(value, copy=True, skip_checks=False)¶

Updates h_matrix, optionally performing sanity checks.

Note that it won’t always be possible to manually specify the h_matrix through this method, specifically if changing the h_matrix could change the nature of the transform. See h_matrix_is_mutable for how you can discover if the h_matrix is allowed to be set for a given class.

Parameters:	value (ndarray) – The new homogeneous matrix to set copy (bool, optional) – If False do not copy the h_matrix. Useful for performance. skip_checks (bool, optional) – If True skip checking. Useful for performance.
Raises:	`NotImplementedError` – If `h_matrix_is_mutable` returns `False`.

composes_with¶: Any Homogeneous can compose with any other Homogeneous.

linear_component¶

The linear component of this affine transform.

Type:	`(n_dims, n_dims)` ndarray

n_parameters¶

The number of parameters: n_dims

Type:	int

translation_component¶

The translation component of this affine transform.

Type:	`(n_dims,)` ndarray