Transform¶
-
class
menpo.transform.
Transform
[source]¶ Bases:
Copyable
Abstract representation of any spatial transform.
Provides a unified interface to apply the transform with
apply_inplace()
andapply()
.All Transforms support basic composition to form a
TransformChain
.There are two useful forms of composition. Firstly, the mathematical composition symbol o has the following definition:
let a(x) and b(x) be two transforms on x. (a o b)(x) == a(b(x))
This functionality is provided by the
compose_after()
family of .. method:: (a.compose_after(b)).apply(x) == a.apply(b.apply(x))Equally useful is an inversion the order of composition - so that over time a large chain of transforms can be built to do a useful job, and composing on this chain adds another transform to the end (after all other preceding transforms have been performed).
For instance, let’s say we want to rescale a
PointCloud
p
around it’s mean, and then translate it some place else. It would be nice to be able to do something like:t = Translation(-p.centre) # translate to centre s = Scale(2.0) # rescale move = Translate([10, 0 ,0]) # budge along the x axis t.compose(s).compose(-t).compose(move)
In Menpo, this functionality is provided by the
compose_before()
family of methods:(a.compose_before(b)).apply(x) == b.apply(a.apply(x))
For native composition, see the
ComposableTransform
subclass and theVComposable
mix-in. For inversion, see theInvertible
andVInvertible
mix-ins. For alignment, see theAlignment
mix-in.-
apply
(x, **kwargs)[source]¶ Applies this transform to
x
.If
x
isTransformable
,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- x (
-
apply_inplace
(x, **kwargs)[source]¶ Applies this transform to a
Transformable
x
destructively.Any
kwargs
will be passed to the specific transform_apply()
method.Parameters: - x (
Transformable
) – TheTransformable
object to be transformed. - kwargs (dict) – Passed through to
_apply()
.
Returns: transformed (
type(x)
) – The transformed object- x (
-
compose_after
(transform)[source]¶ Returns a
TransformChain
that represents this transform composed after the given transform:c = a.compose_after(b) c.apply(p) == a.apply(b.apply(p))
a
andb
are left unchanged.This corresponds to the usual mathematical formalism for the compose operator, o.
Parameters: - transform (
TransformChain
) – Transform to be applied before self - Returns –
- -------- –
- transform – The resulting transform chain.
- transform (
-
compose_before
(transform)[source]¶ Returns a
TransformChain
that represents this transform composed before the given transform:c = a.compose_before(b) c.apply(p) == b.apply(a.apply(p))
a
andb
are left unchanged.Parameters: - transform (
TransformChain
) – Transform to be applied after self - Returns –
- -------- –
- transform – The resulting transform chain.
- transform (
-
copy
()¶ Generate an efficient copy of this object.
Note that Numpy arrays and other
Copyable
objects onself
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
-
n_dims
¶ The dimensionality of the data the transform operates on.
None if the transform is not dimension specific.
Type: int or None
-
n_dims_output
¶ The output of the data from the transform.
None if the output of the transform is not dimension specific.
Type: int or None
-